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https://api.github.com/repos/huggingface/datasets/issues/3189 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3189/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3189/comments | https://api.github.com/repos/huggingface/datasets/issues/3189/events | https://github.com/huggingface/datasets/issues/3189 | 1,041,044,986 | I_kwDODunzps4-DRX6 | 3,189 | conll2003 incorrect label explanation | {
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"Hi @BramVanroy,\r\n\r\nsince these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:\r\n```python\r\ndset.features[field_name].feature.names # .feature because it's a sequence of labels\r\n```\r\n\r\nand to find the mapping between names and integers, use: \r\n```python\r\ndset.features[field_name].feature.int2str(value_or_values_list) # map integer value to string value\r\n# or\r\ndset.features[field_name].feature.str2int(value_or_values_list) # map string value to integer value\r\n```\r\n\r\n"
] | 1,635,764,610,000 | 1,636,454,458,000 | 1,636,454,458,000 | CONTRIBUTOR | null | In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging. | {
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https://api.github.com/repos/huggingface/datasets/issues/3188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3188/comments | https://api.github.com/repos/huggingface/datasets/issues/3188/events | https://github.com/huggingface/datasets/issues/3188 | 1,040,980,712 | I_kwDODunzps4-DBro | 3,188 | conll2002 issues | {
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"Hi ! Thanks for reporting :)\r\n\r\nThis is related to https://github.com/huggingface/datasets/issues/2742, I'm working on it. It should fix the viewer for around 80 datasets.\r\n",
"Ah, hadn't seen that sorry.\r\n\r\nThe scrambled \"point of contact\" is a separate issue though, I think.",
"@lhoestq The \"point of contact\" is still an issue.",
"It will be fixed in https://github.com/huggingface/datasets/pull/3274, thanks"
] | 1,635,760,164,000 | 1,636,984,259,000 | 1,636,737,491,000 | CONTRIBUTOR | null | **Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did | {
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https://api.github.com/repos/huggingface/datasets/issues/3187 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3187/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3187/comments | https://api.github.com/repos/huggingface/datasets/issues/3187/events | https://github.com/huggingface/datasets/pull/3187 | 1,040,412,869 | PR_kwDODunzps4t44Ab | 3,187 | Add ChrF(++) (as implemented in sacrebleu) | {
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} | [] | closed | false | null | [] | null | [] | 1,635,670,438,000 | 1,635,864,650,000 | 1,635,863,486,000 | CONTRIBUTOR | null | Similar to my [PR for TER](https://github.com/huggingface/datasets/pull/3153), it feels only right to also include ChrF and friends. These are present in Sacrebleu and are therefore very similar to implement as TER and sacrebleu. I tested the implementation with sacrebleu's tests to verify. You can try this below for yourself
```python
import datasets
EPSILON = 1e-4
chrf = datasets.load_metric(r"path\to\datasets\metrics\chrf")
test_cases = [
(["abcdefg"], ["hijklmnop"], 0.0),
(["a"], ["b"], 0.0),
([""], ["b"], 0.0),
([""], ["ref"], 0.0),
([""], ["reference"], 0.0),
(["aa"], ["ab"], 8.3333),
(["a", "b"], ["a", "c"], 8.3333),
(["a"], ["a"], 16.6667),
(["a b c"], ["a b c"], 50.0),
(["a b c"], ["abc"], 50.0),
([" risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."],
["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."], 63.361730),
([" Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich. "],
["Das VerhΓ€ltnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."], 64.1302698),
(["Niemand hat die Absicht, eine Mauer zu errichten"], ["Niemand hat die Absicht, eine Mauer zu errichten"], 100.0),
]
for hyp, ref, score in test_cases:
# Note the reference transformation which is different from scarebleu's input format
results = chrf.compute(predictions=hyp, references=[[r] for r in ref],
char_order=6, word_order=0, beta=3, eps_smoothing=True)
if abs(score - results["score"]) > EPSILON:
print(f"expected {score}, got {results['score']} for {hyp} - {ref}")
test_cases_effective_order = [
(["a"], ["a"], 100.0),
([""], ["reference"], 0.0),
(["a b c"], ["a b c"], 100.0),
(["a b c"], ["abc"], 100.0),
([""], ["c"], 0.0),
(["a", "b"], ["a", "c"], 50.0),
(["aa"], ["ab"], 25.0),
]
for hyp, ref, score in test_cases_effective_order:
# Note the reference transformation which is different from scarebleu's input format
results = chrf.compute(predictions=hyp, references=[[r] for r in ref],
char_order=6, word_order=0, beta=3, eps_smoothing=False)
if abs(score - results["score"]) > EPSILON:
print(f"expected {score}, got {results['score']} for {hyp} - {ref}")
test_cases_keep_whitespace = [
(
["Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich."],
["Das VerhΓ€ltnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."],
67.3481606,
),
(
["risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."],
["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."],
65.2414427,
),
]
for hyp, ref, score in test_cases_keep_whitespace:
# Note the reference transformation which is different from scarebleu's input format
results = chrf.compute(predictions=hyp, references=[[r] for r in ref],
char_order=6, word_order=0, beta=3,
whitespace=True)
if abs(score - results["score"]) > EPSILON:
print(f"expected {score}, got {results['score']} for {hyp} - {ref}")
predictions = ["The relationship between Obama and Netanyahu is not exactly friendly."]
references = [["The ties between Obama and Netanyahu are not particularly friendly."]]
print(chrf.compute(predictions=predictions, references=references))
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3186 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3186/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3186/comments | https://api.github.com/repos/huggingface/datasets/issues/3186/events | https://github.com/huggingface/datasets/issues/3186 | 1,040,369,397 | I_kwDODunzps4-Asb1 | 3,186 | Dataset viewer for nli_tr | {
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] | open | false | null | [] | null | [] | 1,635,652,593,000 | 1,635,652,593,000 | null | CONTRIBUTOR | null | ## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
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**Link:** https://huggingface.co/datasets/samsum
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet
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"@lhoestq Thanks for the review. I totally understand what you are saying. Normally, I would definitely agree with you, but in this particular case, the quality of v1 is poor, and the dataset itself is small (at the time we created v1 it was the only RO NER dataset, and its size was limited by the available resources). \r\n\r\nThis is why we worked to build a larger one, with much better inter-annotator agreement. Fact is, models trained on v1 will be of very low quality and I would not recommend to anybody to use/do that. That's why I'd strongly suggest we replace v1 with v2, and kindof make v1 vanish :) \r\n\r\nWhat do you think? If you insist on having v1 accessible, I'll add the required code. Thanks!\r\n\r\n",
"Ok I see ! I think it's fine then, no need to re-add V1"
] | 1,635,591,003,000 | 1,635,868,943,000 | 1,635,868,942,000 | CONTRIBUTOR | null | Hi, as we've recently finished with the new RONEC (Romanian Named Entity Corpus), we'd like to update the dataset here as well. It's actually essential as links to V1 are no longer valid.
In reality we'd like to replace completely v1, as v2 is a full re-annotation of v1 with additional data (up to 2x size vs v1).
I've run the make style and all the dummy and real data test, and they passed.
I hope it's okay to merge the new RONEC v2 in the datasets.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/3183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3183/comments | https://api.github.com/repos/huggingface/datasets/issues/3183/events | https://github.com/huggingface/datasets/pull/3183 | 1,039,761,120 | PR_kwDODunzps4t3Dag | 3,183 | Add missing docstring to DownloadConfig | {
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https://api.github.com/repos/huggingface/datasets/issues/3182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3182/comments | https://api.github.com/repos/huggingface/datasets/issues/3182/events | https://github.com/huggingface/datasets/pull/3182 | 1,039,739,606 | PR_kwDODunzps4t2-9J | 3,182 | Don't memoize strings when hashing since two identical strings may have different python ids | {
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"This change slows down the hash computation a little bit but from my tests it doesn't look too impactful. So I think it's fine to merge this."
] | 1,635,524,777,000 | 1,635,845,738,000 | 1,635,845,737,000 | MEMBER | null | When hashing an object that has several times the same string, the hashing could return a different hash if the identical strings share the same python `id()` or not.
Here is an example code that shows how the issue can affect the caching:
```python
import json
import pyarrow as pa
from datasets.features import Features
from datasets.fingerprint import Hasher
schema = pa.schema([pa.field("some_string", pa.string()), pa.field("another_string", pa.string())])
features_from_schema = Features.from_arrow_schema(schema)
Hasher.hash(features_from_schema) # dffa9dca9a73fd8c
features_dict = json.loads('{"some_string": {"dtype": "string", "id": null, "_type": "Value"}, "another_string": {"dtype": "string", "id": null, "_type": "Value"}}')
features_from_json = Features.from_dict(features_dict)
Hasher.hash(features_from_json) # 3812e76b15e6420e
features_from_schema == features_from_json # True
```
This is because in `features_dict`, some strings like "dtype" are repeated but don't share the same id, contrary to the ones in `features_from_schema`.
I fixed that by disabling memoization for strings.
This could be optimized in the future by implementing a smarter memoization with a special handling for strings. | {
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https://api.github.com/repos/huggingface/datasets/issues/3181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3181/comments | https://api.github.com/repos/huggingface/datasets/issues/3181/events | https://github.com/huggingface/datasets/issues/3181 | 1,039,682,097 | I_kwDODunzps49-Eox | 3,181 | `None` converted to `"None"` when loading a dataset | {
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"Hi @eladsegal, thanks for reporting.\r\n\r\n@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.\r\n\r\nAll values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value(\"bool\")`, `None` is casted to `False`.\r\n\r\nIt is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158).",
"Thanks for reporting.\r\n\r\nThis is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https://github.com/huggingface/datasets/pull/3158 to keep the None values and avoid this breaking change ?\r\n\r\nEDIT: the other types (bool, int, etc) can also become nullable IMO",
"So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.\r\nUsing the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all.",
"Hi @eladsegal,\r\n\r\nUse `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e\r\n```\r\n\r\nI'm making all the feature types nullable as we speak, and the fix will be merged probably early next week.",
"Hi @mariosasko, is there an estimation as to when this issue will be fixed?",
"https://github.com/huggingface/datasets/pull/3195 fixed it, we'll do a new release soon :)\r\n\r\nFor now feel free to install `datasets` from the master branch",
"Thanks, but unfortunately looks like it isn't fixed yet π’ \r\n[notebook for 1.14.0](https://colab.research.google.com/drive/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)\r\n[notebook for master](https://colab.research.google.com/drive/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing)",
"Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick.",
"Thank you, it works! π "
] | 1,635,521,033,000 | 1,639,185,400,000 | 1,639,060,017,000 | CONTRIBUTOR | null | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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"heck, test failings. moving to draft. will come back to this later today hopefully",
"Thanks for fixing this :)\r\nI just updated the dataset_infos.json and added the missing `pretty_name` tag to the dataset card",
"thank you @lhoestq! running around as always it felt through as a lower priority..."
] | 1,635,518,544,000 | 1,635,860,467,000 | 1,635,849,432,000 | MEMBER | null | Fixing label mapping for hlgd.
0 correponds to same event and 1 corresponds to different event
<img width="642" alt="Capture dβeΜcran 2021-10-29 aΜ 10 39 58 AM" src="https://user-images.githubusercontent.com/16107619/139454810-1f225e3d-ad48-44a8-b8b1-9205c9533839.png">
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"The issue is that the datasets are malformed. Not a bug with the datasets library"
] | 1,635,514,247,000 | 1,635,514,521,000 | 1,635,514,521,000 | CONTRIBUTOR | null | ## Describe the bug
After https://github.com/huggingface/datasets/pull/3159, we can get the config name of "Check/region_1", which is "Check___region_1".
But now we cannot load the dataset (not sure it's related to the above PR though). It's the case for all the similar datasets, listed in https://github.com/huggingface/datasets-preview-backend/issues/78
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_config_names
>>> get_dataset_config_names("Check/region_1")
['Check___region_1']
>>> load_dataset("Check/region_1")
Using custom data configuration Check___region_1-d2b3bc48f11c9be2
Downloading and preparing dataset json/Check___region_1 to /home/slesage/.cache/huggingface/datasets/json/Check___region_1-d2b3bc48f11c9be2/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 4443.12it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 1277.19it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "builder_name" does not exist in table schema'
```
Loading in streaming mode also returns something strange:
```python
>>> list(load_dataset("Check/region_1", streaming=True, split="train"))
Using custom data configuration Check___region_1-d2b3bc48f11c9be2
[{'builder_name': None, 'citation': '', 'config_name': None, 'dataset_size': None, 'description': '', 'download_checksums': None, 'download_size': None, 'features': {'speech': {'feature': {'dtype': 'float64', 'id': None, '_type': 'Value'}, 'length': -1, 'id': None, '_type': 'Sequence'}, 'sampling_rate': {'dtype': 'int64', 'id': None, '_type': 'Value'}, 'label': {'dtype': 'string', 'id': None, '_type': 'Value'}}, 'homepage': '', 'license': '', 'post_processed': None, 'post_processing_size': None, 'size_in_bytes': None, 'splits': None, 'supervised_keys': None, 'task_templates': None, 'version': None}, {'_data_files': [{'filename': 'dataset.arrow'}], '_fingerprint': 'f1702bb5533c549c', '_format_columns': ['speech', 'sampling_rate', 'label'], '_format_kwargs': {}, '_format_type': None, '_indexes': {}, '_indices_data_files': None, '_output_all_columns': False, '_split': None}]
```
## Expected results
The dataset should be loaded
## Actual results
An error occurs
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Linux-5.11.0-1020-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3178 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3178/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3178/comments | https://api.github.com/repos/huggingface/datasets/issues/3178/events | https://github.com/huggingface/datasets/issues/3178 | 1,039,539,076 | I_kwDODunzps499huE | 3,178 | "Property couldn't be hashed properly" even though fully picklable | {
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] | open | false | null | [] | null | [
"After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https://dill.readthedocs.io/en/latest/dill.html#dill._dill.dumps) about this:\r\n\r\n> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().\r\n\r\nIn the utils, this is explicitly enabled\r\n\r\nhttps://github.com/huggingface/datasets/blob/df63614223bf1dd1feb267d39d741bada613352c/src/datasets/utils/py_utils.py#L327-L330\r\n\r\nIs this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.)",
"Hi ! Thanks for reporting\r\n\r\nYes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function\r\n\r\nEDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https://github.com/huggingface/datasets/issues/3044#issuecomment-948818210)",
"I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. ",
"@lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https://github.com/BramVanroy/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.\r\n\r\n```python\r\nfrom functools import partial\r\nfrom pathlib import Path\r\n\r\nimport spacy\r\nfrom datasets import Dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\ndef main():\r\n fin = r\"some/file/with/many/lines\"\r\n lines = Path(fin).read_text(encoding=\"utf-8\").splitlines()\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n ds = Dataset.from_dict({\"text\": lines, \"text_id\": list(range(len(lines)))})\r\n tok = partial(tokenize, nlp)\r\n ds = ds.map(tok, load_from_cache_file=True)\r\n print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```\r\n\r\n... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):\r\n\r\n```python\r\nfrom functools import partial\r\n\r\nimport spacy\r\nfrom datasets import load_dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, sample):\r\n return {\"tok\": [t.text for t in nlp(sample[\"text\"])]}\r\n\r\ndef main():\r\n fin = r\"some/file/with/many/lines\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n tok_func = partial(tokenize, nlp)\r\n ds = load_dataset('text', data_files=fin)\r\n ds = ds[\"train\"].map(tok_func)\r\n print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```",
"It looks like every time you load `en_core_web_sm` you get a different python object:\r\n```python\r\nimport spacy\r\nfrom datasets.fingerprint import Hasher\r\n\r\nnlp1 = spacy.load(\"en_core_web_sm\")\r\nnlp2 = spacy.load(\"en_core_web_sm\")\r\nHasher.hash(nlp1), Hasher.hash(nlp2)\r\n# ('f6196a33882fea3b', 'a4c676a071f266ff')\r\n```\r\nHere is a list of attributes that have different hashes for `nlp1` and `nlp2`:\r\n- tagger\r\n- parser\r\n- entity\r\n- pipeline (it's the list of the three attributes above)\r\n\r\nI just took a look at the tagger for example and I found subtle differences (there may be other differences though):\r\n```python\r\nnlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id\r\n# (1721, 2243)\r\n```\r\n\r\nWe can try to find all the differences and find the best way to hash those objects properly",
"Thanks for searching! I went looking, and found that this is an implementation detail of thinc\r\n\r\nhttps://github.com/explosion/thinc/blob/68691e303ae68cae4bc803299016f1fc064328bf/thinc/model.py#L96-L98\r\n\r\nPresumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).\r\n\r\nDo you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher.",
"It can be even simpler to hash the bytes of the pipeline instead\r\n```python\r\nnlp1.to_bytes() == nlp2.to_bytes() # True\r\n```\r\n\r\nIMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).\r\nWhat could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`/`from_bytes` to have deterministic pickle dumps.\r\n\r\nFinally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.\r\n```python\r\n@datasets.register_hash(spacy.language.Language)\r\ndef hash_spacy_language(nlp):\r\n return Hasher.hash(nlp.to_bytes())\r\n```",
"I do not quite understand what you mean. as far as I can tell, using `to_bytes` does a pickle dump behind the scene (with `srsly`), recursively using `to_bytes` on the required objects. Therefore, the result of `to_bytes` is a deterministic pickle dump AFAICT. Or do you mean that you wish that using your own pickler and running `dumps(nlp)` should also be deterministic? I guess that would require `__setstate__` and `__getstate__` methods on all the objects that have to/from_bytes. I'll have a listen over at spaCy what they think, and if that would solve the issue. I'll try this locally first, if I find the time.\r\n\r\nI agree that having the option to use a custom hasher would be useful. I like your suggestion!\r\n\r\nEDIT: after trying some things and reading through their API, it seems that they explicitly do not want this. https://spacy.io/usage/saving-loading#pipeline\r\n\r\n> When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them β not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the config, which contains the pipeline configuration and all the relevant settings.\r\n\r\nBest way forward therefore seems to implement the ability to specify a hasher depending on the objects that are pickled, as you suggested. I can work on this if that is useful. I could use some pointers as to how you would like to implement the `register_hash` functionality though. I assume using `catalogue` over at Explosion might be a good starting point.\r\n\r\n",
"Interestingly, my PR does not solve the issue discussed above. The `tokenize` function hash is different on every run, because for some reason `nlp.__call__` has a different hash every time. The issue therefore seems to run much deeper than I thought. If you have any ideas, I'm all ears.\r\n\r\n```shell\r\ngit clone https://github.com/explosion/spaCy.git\r\ncd spaCy/\r\ngit checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf\r\ncd ..\r\n\r\ngit clone https://github.com/BramVanroy/datasets.git\r\ncd datasets\r\ngit checkout registry\r\npip install -e .\r\npip install ../spaCy\r\nspacy download en_core_web_sm\r\n```\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\nfrom datasets.utils.registry import hashers\r\n\r\n@hashers.register(spacy.Language)\r\ndef hash_spacy_language(nlp):\r\n return Hasher.hash(nlp.to_bytes())\r\n\r\ndef main():\r\n fin = r\"your/large/file\"\r\n nlp = spacy.load(\"en_core_web_sm\")\r\n # This is now always the same yay!\r\n print(Hasher.hash(nlp))\r\n\r\n def tokenize(l):\r\n return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n ds = load_dataset(\"text\", data_files=fin)\r\n # But this is not...\r\n print(Hasher.hash(tokenize))\r\n # ... because of this\r\n print(Hasher.hash(nlp.__call__))\r\n ds = ds[\"train\"].map(tokenize)\r\n print(ds[0:2])\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```",
"Hi ! I just answered in your PR :) In order for your custom hashing to be used for nested objects, you must integrate it into our recursive pickler that we use for hashing.",
"I don't quite understand the design constraints of `datasets` or the script that you're running, but my usual advice is to avoid using pickle unless you _absolutely_ have to. So for instance instead of doing your `partial` over the `nlp` object itself, can you just pass the string `en_core_web_sm` in? This will mean calling `spacy.load()` inside the work function, but this is no worse than having to call `pickle.load()` on the contents of the NLP object anyway -- in fact you'll generally find `spacy.load()` faster, apart from the disk read.\r\n\r\nIf you need to pass in the bytes data and don't want to read from disk, you could do something like this:\r\n\r\n```\r\nmsg = (nlp.lang, nlp.to_bytes())\r\n\r\ndef unpack(lang, bytes_data):\r\n return spacy.blank(lang).from_bytes(bytes_data)\r\n```\r\n\r\nI think that should probably work: the Thinc `model.to_dict()` method (which is used by the `model.to_bytes()` method) doesn't pack the model's ID into the message, so the `nlp.to_bytes()` that you get shouldn't be affected by the global IDs. So you should get a clean message from `nlp.to_bytes()` that doesn't depend on the global state.",
"Hi Matthew, thanks for chiming in! We are currently implementing exactly what you suggest: `to_bytes()` as a default before pickling - but we may prefer `to_dict` to avoid double dumping.\r\n\r\n`datasets` uses pickle dumps (actually dill) to get unique representations of processing steps (a \"fingerprint\" or hash). So it never needs to re-load that dump - it just needs its value to create a hash. If a fingerprint is identical to a cached fingerprint, then the result can be retrieved from the on-disk cache. (@lhoestq or @mariosasko can correct me if I'm wrong.)\r\n\r\nI was experiencing the issue that parsing with spaCy gave me a different fingerprint on every run of the script and thus it could never load the processed dataset from cache. At first I thought the reason was that spaCy Language objects were not picklable with recursive dill, but even after [adjusting for that](https://github.com/explosion/spaCy/pull/9593) the issue persisted. @lhoestq found that this is due to the changing `id`, which you discussed [here](https://github.com/explosion/spaCy/discussions/9609#discussioncomment-1661081). So yes, you are right. On the surface there simply seems to be an incompatibility between `datasets` default caching functionality as it is currently implemented and `spacy.Language`.\r\n\r\nThe [linked PR](https://github.com/huggingface/datasets/pull/3224) aims to remedy that, though. Up to now I have put some effort into making it easier to define your own \"pickling\" function for a given type (and optionally any of its subclasses). That allows us to tell `datasets` that instead of doing `dill.save(nlp)` (non-deterministic), to use `dill.save(nlp.to_bytes())` (deterministic). When I find some more time, the PR [will be expanded](https://github.com/huggingface/datasets/pull/3224#issuecomment-968958528) to improve the user-experience a bit and add a built-in function to pickle `spacy.Language` as one of the defaults (using `to_bytes()`)."
] | 1,635,512,169,000 | 1,637,228,011,000 | null | CONTRIBUTOR | null | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3177 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3177/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3177/comments | https://api.github.com/repos/huggingface/datasets/issues/3177/events | https://github.com/huggingface/datasets/issues/3177 | 1,039,487,780 | I_kwDODunzps499VMk | 3,177 | More control over TQDM when using map/filter with multiple processes | {
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"Hi,\r\n\r\nIt's hard to provide an API that would cover all use-cases with tqdm in this project.\r\n\r\nHowever, you can make it work by defining a custom decorator (a bit hacky tho) as follows:\r\n```python\r\nimport datasets\r\n\r\ndef progress_only_on_rank_0(func):\r\n def wrapper(*args, **kwargs):\r\n rank = kwargs.get(\"rank\")\r\n disable_tqdm = kwargs.get(\"disable_tqdm\", False)\r\n disable_tqdm = True if rank is not None and rank > 0 else disable_tqdm\r\n kwargs[\"disable_tqdm\"] = disable_tqdm\r\n return func(*args, **kwargs)\r\n return wrapper\r\n \r\ndatasets.Dataset._map_single = progress_only_on_rank_0(datasets.Dataset._map_single)\r\n``` \r\n\r\nEDIT: Ups, closed by accident.\r\n\r\nThanks for the provided links. `Trainer` requires this for training in multi-node distributed setting. However, `Dataset.map` doesn't support that yet.\r\n\r\nDo you have an API for this in mind? `Dataset.map` is already bloated with the arguments, so IMO it's not a good idea to add a new arg there.\r\n\r\n",
"Inspiration may be found at `transformers`.\r\n\r\nhttps://github.com/huggingface/transformers/blob/4a394cf53f05e73ab9bbb4b179a40236a5ffe45a/src/transformers/trainer.py#L1231-L1233\r\n\r\nTo get unique IDs for each worker, see https://stackoverflow.com/a/10192611/1150683"
] | 1,635,508,576,000 | 1,635,853,130,000 | null | CONTRIBUTOR | null | It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.
```python
dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6)
```
The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.
```
#0: 0%| | 0/13 [00:00<?, ?ba/s]
#1: 0%| | 0/13 [00:00<?, ?ba/s]
#2: 0%| | 0/13 [00:00<?, ?ba/s]
#3: 0%| | 0/13 [00:00<?, ?ba/s]
#4: 0%| | 0/13 [00:00<?, ?ba/s]
#5: 0%| | 0/13 [00:00<?, ?ba/s]
#0: 8%| | 1/13 [00:00<?, ?ba/s]
#1: 8%| | 1/13 [00:00<?, ?ba/s]
...
```
Instead, it would be welcome if we had the option to only show the progress of rank 0. | {
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https://api.github.com/repos/huggingface/datasets/issues/3176 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3176/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3176/comments | https://api.github.com/repos/huggingface/datasets/issues/3176/events | https://github.com/huggingface/datasets/pull/3176 | 1,039,068,312 | PR_kwDODunzps4t00xS | 3,176 | OpenSLR dataset: update generate_examples to properly extract data for SLR83 | {
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"Also fix #3125."
] | 1,635,469,167,000 | 1,636,042,845,000 | 1,635,501,849,000 | CONTRIBUTOR | null | Fixed #3168.
The SLR38 indices are CSV files and there wasn't any code in openslr.py to process these files properly. The end result was an empty table.
I've added code to properly process these CSV files. | {
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https://api.github.com/repos/huggingface/datasets/issues/3175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3175/comments | https://api.github.com/repos/huggingface/datasets/issues/3175/events | https://github.com/huggingface/datasets/pull/3175 | 1,038,945,271 | PR_kwDODunzps4t0bXw | 3,175 | Add docs for `to_tf_dataset` | {
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"This looks great, thank you!",
"Thanks !\r\n\r\nFor some reason the new GIF is 6MB, which is a bit heavy for an image on a website. The previous one was around 200KB though which is perfect. For a good experience we usually expect images to be less than 500KB - otherwise for users with poor connection it takes too long to load. Could you try to reduce its size ? Than I think we can merge :)"
] | 1,635,454,522,000 | 1,635,953,976,000 | 1,635,934,043,000 | CONTRIBUTOR | null | This PR adds some documentation for new features released in v1.13.0, with the main addition being `to_tf_dataset`:
- Show how to use `to_tf_dataset` in the tutorial, and move `set_format(type='tensorflow'...)` to the Process section (let me know if I'm missing anything @Rocketknight1 π
).
- Add an example for loading dataset from multiple zipped CSV files to the Load section.
- Add an example for removing columns for an `IterableDataset`.
- Add graphic for visualizing streaming. | {
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https://api.github.com/repos/huggingface/datasets/issues/3174 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3174/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3174/comments | https://api.github.com/repos/huggingface/datasets/issues/3174/events | https://github.com/huggingface/datasets/pull/3174 | 1,038,427,245 | PR_kwDODunzps4tyuQ_ | 3,174 | Asserts replaced by exceptions (huggingface#3171) | {
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"Your first PR went smoothly, well done!\r\nYou are welcome to continue contributing to this project.\r\nGrΓ cies, @joseporiolayats! π "
] | 1,635,422,145,000 | 1,636,180,532,000 | 1,635,512,923,000 | CONTRIBUTOR | null | I've replaced two asserts with their proper exceptions following the guidelines described in issue #3171 by following the contributing guidelines.
PS: This is one of my first PRs, hoping I don't break anything! | {
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https://api.github.com/repos/huggingface/datasets/issues/3173 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3173/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3173/comments | https://api.github.com/repos/huggingface/datasets/issues/3173/events | https://github.com/huggingface/datasets/pull/3173 | 1,038,404,300 | PR_kwDODunzps4typcA | 3,173 | Fix issue with filelock filename being too long on encrypted filesystems | {
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Fix #2924
cc: @lmmx | {
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"NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.",
"Hi,\r\n\r\nIt's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.\r\nAs you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.\r\n\r\nLet me know if this helps.",
"Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue. \r\n\r\nI did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`. Just FYI, it looks like:\r\n\r\n```python\r\nclass Builder():\r\n def __call__() -> DatasetDict:\r\n # load and preprocess the data\r\n return dataset\r\n\r\nclass DataModule(LightningDataModule):\r\n def prepare_data():\r\n self.builder()\r\n def setup():\r\n self.dataset = self.builder()\r\n```\r\n\r\nUnfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.\r\n\r\nThe culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project structure was:\r\n\r\n```\r\nsrc/\r\n | - cli.py\r\n | - training/\r\n | -experiment.py\r\n\r\n# code in experiment.py\r\ndef run_experiment(config):\r\n # preprocess data and run\r\n \r\n# code in cli.py\r\n@hydra.main(...)\r\ndef run(config):\r\n return run_experiment(config)\r\n```\r\n\r\nMoving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why. \r\n\r\nEven if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!"
] | 1,635,416,940,000 | 1,635,938,872,000 | 1,635,938,770,000 | NONE | null | ## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>βββββββββββββββββββββββββββββββββββββββββββββββββββ| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
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"Adding the remaining tasks for this issue to help new code contributors. \r\n$ cd src/datasets && ack assert -lc \r\n- [x] commands/convert.py:1\r\n- [x] arrow_reader.py:3\r\n- [x] load.py:7\r\n- [x] utils/py_utils.py:2\r\n- [x] features/features.py:9\r\n- [x] arrow_writer.py:7\r\n- [x] search.py:6\r\n- [x] table.py:1\r\n- [x] metric.py:3\r\n- [x] tasks/image_classification.py:1\r\n- [x] arrow_dataset.py:17\r\n- [x] fingerprint.py:6\r\n- [x] io/json.py:1\r\n- [x] io/csv.py:1",
"Hi all,\r\nI am interested in taking up `fingerprint.py`, `search.py`, `arrow_writer.py` and `metric.py`. Will raise a PR soon!",
"Let me look into `arrow_dataset.py`, `table.py`, `data_files.py` & `features.py` ",
"All the tasks are completed for this issue. This can be closed. "
] | 1,635,359,212,000 | 1,640,277,637,000 | 1,640,277,637,000 | CONTRIBUTOR | null | Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR. | {
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https://api.github.com/repos/huggingface/datasets/issues/3170 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3170/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3170/comments | https://api.github.com/repos/huggingface/datasets/issues/3170/events | https://github.com/huggingface/datasets/pull/3170 | 1,037,601,926 | PR_kwDODunzps4twDUo | 3,170 | Preserve ordering in `zip_dict` | {
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} | [] | closed | false | null | [] | null | [] | 1,635,350,850,000 | 1,635,512,977,000 | 1,635,512,977,000 | CONTRIBUTOR | null | Replace `set` with the `unique_values` generator in `zip_dict`.
This PR fixes the problem with the different ordering of the example keys across different Python sessions caused by the `zip_dict` call in `Features.decode_example`. | {
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"I've also added environment variable configuration so that this can be configured once per machine (e.g. in a `.bashrc` file), as is already done for a few other config variables here.",
"Cancelling PR in favour of @mariosasko's in #3173"
] | 1,635,285,175,000 | 1,635,437,654,000 | 1,635,437,653,000 | NONE | null | Resolve #2924 (https://github.com/huggingface/datasets/issues/2924#issuecomment-952330956) wherein the assumption of file lock maximum filename length to be 255 raises an OSError on encrypted drives (ecryptFS on Linux uses part of the lower filename, reducing the maximum filename size to 143). Allowing this limit to be set in the config module allows this to be modified by users. Will not affect Windows users, as their class passes 255 on init explicitly.
Reproduced with the following example ([the first few lines of a script from Lightning Flash](https://lightning-flash.readthedocs.io/en/latest/reference/speech_recognition.html), fine-tuning a HF model):
```py
import torch
import flash
from flash.audio import SpeechRecognition, SpeechRecognitionData
from flash.core.data.utils import download_data
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip", "./data")
datamodule = SpeechRecognitionData.from_json(
input_fields="file",
target_fields="text",
train_file="data/timit/train.json",
test_file="data/timit/test.json",
)
```
Which gave this traceback:
```py
Traceback (most recent call last):
File "lf_ft.py", line 10, in <module>
datamodule = SpeechRecognitionData.from_json(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json
return cls.from_data_source(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source
train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets
train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset
data = load_data(data, mock_dataset)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data
dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1599, in load_dataset
builder_instance = load_dataset_builder(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1457, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/builder.py", line 285, in __init__
with FileLock(lock_path):
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 323, in __enter__
self.acquire()
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 272, in acquire
self._acquire()
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 403, in _acquire
fd = os.open(self._lock_file, open_mode)
OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock'
```
Note the filename is 145 chars long:
```
>>> len("_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock")
145
```
After installing datasets as an editable local package and modifying the script I was running to first include:
```py
import datasets
datasets.config.MAX_DATASET_CONFIG_ID_READABLE_LENGTH = 143
```
The error goes away.
If I instead deliberately set the value incorrectly as 144, the OSError returns:
```
Traceback (most recent call last):
File "lf_ft.py", line 14, in <module>
datamodule = SpeechRecognitionData.from_json(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json
return cls.from_data_source(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source
train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets
train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset
data = load_data(data, mock_dataset)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data
dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})
File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1605, in load_dataset
builder_instance = load_dataset_builder(
File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1463, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/louis/dev/hf_datasets/src/datasets/builder.py", line 285, in __init__
with FileLock(lock_path):
File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 326, in __enter__
self.acquire()
File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 275, in acquire
self._acquire()
File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 406, in _acquire
fd = os.open(self._lock_file, open_mode)
OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-32c812b5c1272d64_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279...-5794079643713042223.lock'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3168 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3168/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3168/comments | https://api.github.com/repos/huggingface/datasets/issues/3168/events | https://github.com/huggingface/datasets/issues/3168 | 1,036,673,263 | I_kwDODunzps49ymDv | 3,168 | OpenSLR/83 is empty | {
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"Hi @tyrius02, thanks for reporting. I see you self-assigned this issue: are you working on this?",
"@albertvillanova Yes. Figured I introduced the broken config, I should fix it too.\r\n\r\nI've got it working, but I'm struggling with one of the tests. I've started a PR so I/we can work through it.",
"Looks like the tests all passed on the PR."
] | 1,635,277,341,000 | 1,635,501,849,000 | 1,635,501,849,000 | CONTRIBUTOR | null | ## Describe the bug
As the summary says, openslr / SLR83 / train is empty.
The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('openslr', 'SLR83')
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 17877
})
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 0
})
})
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.1.dev0 (master HEAD)
- Platform: Ubuntu 20.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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"Hi ! Thanks for reporting :) I think #3280 should fix this",
"I tried with the latest changes from #3280 on google colab and it worked fine :)\r\nWe'll do a new release soon, in the meantime you can use the updated version with:\r\n```python\r\nload_dataset(\"bookcorpusopen\", revision=\"master\")\r\n```",
"Fixed by #3280."
] | 1,635,264,375,000 | 1,637,164,426,000 | 1,637,164,426,000 | CONTRIBUTOR | null | ## Describe the bug
When using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).
This did not happen with 1.4.1.
I tried also `rm -rf ~/.cache/huggingface` but did not help.
Changing python version between 3.7, 3.8 and 3.9 did not help too.
## Steps to reproduce the bug
```python
import datasets
d = datasets.load_dataset('bookcorpusopen')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
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"Sounds good, thanks !"
] | 1,635,262,104,000 | 1,636,104,457,000 | 1,636,104,456,000 | MEMBER | null | In version 1.13, `prepare_module` was deprecated.
This PR adds a deprecation warning and removes it from all the library, using `dataset_module_factory` or `metric_module_factory` instead.
Fix #3165. | {
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Add deprecation warning and remove its usage from all the library. | {
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"Hi @zlucia, I would actually suggest hosting the dataset as a huggingface.co-hosted dataset.\r\n\r\nThe only difference with a \"canonical\"/legacy dataset is that it's nested under an organization (here `stanford` or `stanfordnlp` for instance β completely up to you) but then you can upload your data using git-lfs (unlike \"canonical\" datasets where we don't host the data)\r\n\r\nLet me know if this fits your use case!\r\n\r\ncc'ing @osanseviero @lhoestq and rest of the team π€",
"Hi @zlucia,\r\n\r\nAs @julien-c pointed out, the way to store/host raw data files in our Hub is by using what we call \"community\" datasets:\r\n- either at your personal namespace: `load_dataset(\"zlucia/casehold\")`\r\n- or at an organization namespace: for example, if you create the organization `reglab`, then `load_dataset(\"reglab/casehold\")`\r\n\r\nPlease note that \"canonical\" datasets do not normally store/host their raw data at our Hub, but in a third-party server. For \"canonical\" datasets, we just host the \"loading script\", that is, a Python script that downloads the raw data from a third-party server, creates the HuggingFace dataset from it and caches it locally.\r\n\r\nIn order to create an organization namespace in our Hub, please follow this link: https://huggingface.co/organizations/new\r\n\r\nThere are already many organizations at our Hub (complete list here: https://huggingface.co/organizations), such as:\r\n- Stanford CRFM: https://huggingface.co/stanford-crfm\r\n- Stanford NLP: https://huggingface.co/stanfordnlp\r\n- Stanford CS329S: Machine Learning Systems Design: https://huggingface.co/stanford-cs329s\r\n\r\nAlso note that you in your organization namespace:\r\n- you can add any number of members\r\n- you can store both raw datasets and models, and those can be immediately accessed using `datasets` and `transformers`\r\n\r\nOnce you have created an organization, these are the steps to upload/host a raw dataset: \r\n- The no-code procedure: https://huggingface.co/docs/datasets/upload_dataset.html\r\n- Using the command line (terminal): https://huggingface.co/docs/datasets/share.html#add-a-community-dataset\r\n\r\nPlease, feel free to ping me if you have any further questions or need help.\r\n",
"Ah I see, I think I was unclear whether there were benefits to uploading a canonical dataset vs. a community provided dataset. Thanks for clarifying. I'll see if we want to create an organization namespace and otherwise, will upload the dataset under my personal namespace."
] | 1,635,204,501,000 | 1,635,623,691,000 | 1,635,623,691,000 | NONE | null | I'm interested in sharing the CaseHOLD dataset (https://arxiv.org/abs/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset](https://huggingface.co/docs/datasets/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks! | {
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"Awesome, looking forward to using it :)",
"Few additional comments:\r\n* the current API doesn't meet the requirements mentioned in #3145 (e.g. image mime-type). However, this will be doable soon as we also plan to store image bytes alongside paths in arrow files (see https://github.com/huggingface/datasets/pull/3129#discussion_r738426187). Then, PIL can return the correct mime-type: \r\n ```python\r\n from PIL import Image\r\n import io\r\n\r\n mimetype = Image.open(io.BytesIO(image_bytes)).get_format_mimetype()\r\n ``` \r\n I plan to add this change in a separate PR.\r\n* currently, I'm returning an `np.ndarray` object after decoding for consistency with the Audio feature. However, the vision models from Transformers prefer an `Image` object to avoid the `Image.fromarray` call in the corresponding feature extractors (see [this warning](https://huggingface.co/transformers/master/model_doc/vit.html#transformers.ViTFeatureExtractor.__call__) in the Transformers docs) cc @NielsRogge \r\n\r\nSo I'm not entirely sure whether to return only a NumPy array, only a PIL Image, or both when decoding. The last point worries me because we shouldn't provide an API that leads to a warning in Transformers (in the docs, not in code :)). At the same time, it makes sense to preserve consistency with the Audio feature and return a NumPy array. \r\n\r\nThat's why I would appreciate your opinions on this.",
"That is a good question. Also pinging @nateraw .\r\n\r\nCurrently we only support returning numpy arrays because of numpy/tf/torch/jax formatting features that we have, and to keep things simple. See the [set_format docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format) for more info",
"I don't think centering the discussion on what ViT expects is good, as the vision Transformers model are still in an experimental stage and we can adapt those depending on what you do here :-).\r\n\r\nIMO, the discussion should revolve on what a user will want to do with a vision dataset, and they will want to:\r\n- lazily decode their images\r\n- maybe apply data augmentation (for the training set)\r\n- resize to a fixed shape for batching\r\n\r\nThe libraries that provide step 2 and 3 either use PIL (thinking torchvision) or cv2 (thinking albumentations). NumPy does not have any function to resize an image or do basic data augmentation (like a rotate) so I think it shouldn't be the default format for an image dataset, PIL or cv2 (in an ideal world with the ability to switch between the two depending on what the users prefer) would be better.\r\n\r\nSide note: I will work on the vision integration in Transformers with Niels next month so please keep me in the loop for those awesome new vision features!",
"@sgugger I completely agree with you, especially after trying to convert the `run_image_classification` script from Transformers to use this feature. The current API doesn't seem intuitive there due to the torchvision transforms, which, as you say, prefer PIL over NumPy arrays. \r\n\r\nSo the default format would return `Image` (PIL) / `np.ndarray` (cv2) and `set_format(numpy/tf/pt)` would return image tensors if I understand you correctly. IMO this makes a lot more sense (and flexibility) than the current API.",
"Also, one additional library worth mentioning here is AugLy which supports image file paths and `PIL.Image.Image` objects.",
"That's so nice !\r\n\r\nAlso I couldn't help myself so I've played with it already ^^\r\nI was agreeably surprised that with minor additions I managed to even allow this, which I find very satisfactory:\r\n```python\r\nimport PIL.Image\r\nfrom datasets import Dataset\r\n\r\npath = \"docs/source/imgs/datasets_logo_name.jpg\"\r\n\r\ndataset = Dataset.from_dict({\"img\": [PIL.Image.open(path)]})\r\nprint(dataset.features)\r\n# {'img': Image(id=None)}\r\nprint(dataset[0][\"img\"])\r\n# <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x300 at 0x129DE4AC8>\r\n```\r\n\r\nLet me know if that's a behavior you'd also like to see \r\n\r\nEDIT: just pushed my changes on a branch, you can see the diff [here](https://github.com/mariosasko/datasets-1/compare/add-image-feature...huggingface:image-type-inference) if you want",
"Thanks, @lhoestq! I like your change. Very elegant indeed.\r\n\r\nP.S. I have to write a big comment that explains all the changes/things left to consider. Will do that in the next few days!",
"I'm marking this PR as ready for review.\r\n\r\nThanks to @sgugger's comment, the API is much more flexible now as it decodes images (lazily) as `PIL.Image.Image` objects and supports transforms directly on them.\r\n\r\nAlso, we no longer return paths explicitly (previously, we would return `{\"path\": image_path, \"image\": pil_image}`) for the following reasons:\r\n* what to return when reading an image from an URL or a NumPy array. We could set `path` to `None` in these situations, but IMO we should avoid redundant information.\r\n* returning a dict doesn't match nicely with the requirement of supporting image modifications - what to do if the user modifies both the image path and the image\r\n\r\n(Btw, for the images stored locally, you can access their paths with `dset[idx][\"image\"].filename`, or by avoiding decoding with `paths = [ex[\"path\"] for ex in dset]`. @lhoestq @albertvillanova WDYT about having an option to skip decoding for complex features, e. g. `Audio(decode=False)`? This way, the user can easily access the underlying data.)\r\n\r\nExamples of what you can do:\r\n```python\r\n# load local images\r\ndset = Dataset.from_dict(\"image\": [local_image_path], features=Features({\"images\": Image()}))\r\n# load remote images (we got this for free by adding support for streaming)\r\ndset = Dataset.from_dict(\"image\": [image_url], features=Features({\"images\": Image()}))\r\n# from np.ndarray\r\ndset = Dataset.from_dict({\"image\": [np.array(...)]}, features=Features({\"images\": Image()}))\r\n# cast column\r\ndset = Dataset.from_dict({\"image\": [local_image_path]})\r\ndset.cast_column(\"image\", Image())\r\n\r\n# automatic type inference\r\ndset = Dataset.from_dict({\"image\": [PIL.Image.open(local_image_path)]})\r\n\r\n# transforms\r\ndef img_transform(example):\r\n ...\r\n example[\"image\"] = transformed_pil_image_or_np_ndarray\r\n return example\r\ndset.map(img_trnasform)\r\n\r\n# transform that adds a new column with images (automatic inference of the feature type)\r\ndset.map(lambda ex: {\"image_resized\": ex[\"image\"].resize((100, 100))})\r\nprint(dset.features[\"image_resized\"]) # will print Image()\r\n```\r\n\r\nSome more cool features:\r\n* We store the image filename (`pil_image.filename`) whenever possible to avoid costly conversion to bytes\r\n* if possible, we use native compression when encoding images. Otherwise, we fall back to the lossless PNG format (e.g. after image ops or when storing NumPy arrays)\r\n\r\nHints to make reviewing easier:\r\n* feel free to ignore the extension type part because it's related to PyArrow internals.\r\n* also, let me know if we are too strict/ too flexible in terms of types the Image feature can encode/decode. Hints:\r\n * `encode_example` handles encoding during dataset generation (you can think of it as `yield key, features.encode_example(example)`)\r\n * `objects_to_list_of_image_dicts` handles encoding of returned examples in `map`\r\n\r\nP.S. I'll fork the PR branch and start adding the Image feature to the existing image datasets (will also update the `ImageClassification` template while doing that).",
"> WDYT about having an option to skip decoding for complex features, e. g. Audio(decode=False)?\r\n\r\nYes definitely, also I think it could be useful for the dataset viewer to not decode the data but instead return either the bytes or the (possibly chained) URL. cc @severo ",
"We want to merge this today/tomorrow, so I'd really appreciate your reviews @sgugger @nateraw.\r\n\r\nAlso, you can test this feature on the existing image datasets (MNIST, beans, food101, ...) by installing `datasets` from the PR branch:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git@adapt-image-datasets\r\n```\r\n",
"Thanks for the review @nateraw!\r\n\r\n1. This is a copy of your notebook with the fixed map call: https://colab.research.google.com/gist/mariosasko/e351a717682a9392ca03908e65a2600e/image-feature-demo.ipynb\r\n (Sorry for misleading you with the map call in my un-updated notebook)\r\n Also, we can avoid this cast by trying to infer the type of the column (`\"pixel_values\"`) returned by the image feature extractor (we are already doing something similar for the columns with names: `\"attention_mask\"`, `\"input_ids\"`, ...). I plan to add this QOL improvement soon. \r\n2. It should work OK even without updating Pillow and PyArrow (these two libraries are pre-installed in Colab, so updating them requires a restart of the runtime). \r\n > I noticed an error that I'm guessing you ran into when I tried using the older version\r\n\r\n Do you recall which type of error it was because everything works fine on my side if I run the notebooks with the lowest supported version of Pillow (`6.2.1`)?",
"Thanks for playing with it @nateraw and for sharing your notebook, this is useful :)\r\n\r\nI think this is ready now, congrats @mariosasko !",
"Love this feature and hope to release soon!",
"@mariosasko Hi, I got `ArrowNotImplementedError: Unsupported cast from string to extension<arrow.py_extension_type<ImageExtensionType>> (no available cast function for target type)` with following code:\r\n```python\r\ndset = Dataset.from_dict({\"image\": [[\"1.jpg\", \"2.jpg\"]]})\r\ndset.cast_column(\"image\", Sequence(feature=Image()))\r\n# or \r\ndset = Dataset.from_dict({\"image\": [[\"1.png\"]]}, features=Features({\"image\": Sequence(feature=Image())}))\r\n```\r\nHow I can I create a feature with `Sequence` of `Image`"
] | 1,635,188,868,000 | 1,640,358,280,000 | 1,638,812,942,000 | CONTRIBUTOR | null | Adds the Image feature. This feature is heavily inspired by the recently added Audio feature (#2324). Currently, this PR is pretty simple.
Some considerations that need further discussion:
* I've decided to use `Pillow`/`PIL` as the image decoding library. Another candidate I considered is `torchvision`, mostly because of its `accimage` backend, which should be faster for loading `jpeg` images than `Pillow`. However, `torchvision`'s io module only supports png and jpeg images, has `torch` as a hard dependency, and requires magic to work with image bytes ( `torch.ByteTensor(torch.ByteStorage.from_buffer(image_bytes)))`).
* Currently, I'm converting `PIL`'s `Image` type to `np.ndarray`. The vision models in Transformers such as ViT prefer the raw `Image` type and not the decoded tensors, so there is a small overhead due to [this conversion](https://github.com/huggingface/transformers/blob/3e8761ab8077e3bb243fe2f78b2a682bd2257cf1/src/transformers/image_utils.py#L62-L73). IMO this is justified to keep this part aligned with the Audio feature, which also returns `np.ndarray`. What do you think?
* Still have to work on the channel decoding logic:
* PyTorch prefers the channel-first ordering (C, H, W); TF and Flax the channel-last ordering (H, W, C). One cool feature would be adjusting the channel order based on the selected formatter (`torch`, `tf`, `jax`).
* By default, `Image.open` returns images of shape (H, W, C). However, ViT's feature extractor expects the format (C, H, W) if the image is passed as an array (explained [here](https://huggingface.co/transformers/model_doc/vit.html#transformers.ViTFeatureExtractor.__call__)), so I'm more inclined to the format (C, H, W). Which one do you prefer, (C, H, W) or (H, W, C)?
* Are there any options you'd like to see? (the user could change those via `cast_column`, such as `sampling_rate` in the Audio feature)
TODOs:
* [x] tests
* in subsequent PRs:
* docs - a section in the docs, which gives some additional info on the Image and Audio feature and compares them to
`ArrayND`
* streaming (waiting for #3129 and #3133 to get merged first)
* update the image tasks and the datasets to use the new feature
* Image/Audio formatting
[Colab Notebook](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c?usp=sharing) where you can play with this feature.
I'm also adding a link to the [Image](https://github.com/tensorflow/datasets/blob/7ac7d506488d46038a5854961d068926b3f93c7f/tensorflow_datasets/core/features/image_feature.py#L155) feature in TFDS because one of our goals is to parse TFDS scripts eventually, so our Image feature has to (at least) support all the formats theirs does.
Feel free to cc anyone who might be interested.
P.S. Please ignore the changes in the `datasets/**/*.py` files π. | {
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https://api.github.com/repos/huggingface/datasets/issues/3162 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3162/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3162/comments | https://api.github.com/repos/huggingface/datasets/issues/3162/events | https://github.com/huggingface/datasets/issues/3162 | 1,035,462,136 | I_kwDODunzps49t-X4 | 3,162 | `datasets-cli test` should work with datasets without scripts | {
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"> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).\r\n> \r\n> I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!\r\n\r\nwhy don't you try to share that info with people, so you can also save some days.",
"Hi ! You can run the command if you download the repository\r\n```\r\ngit clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest\r\n```\r\nand run the command\r\n```\r\ndatasets-cli test DataMeasurementsTest/DataMeasurementsTest.py\r\n```\r\n\r\n(though on my side it doesn't manage to download the data since the dataset is private ^^)",
"> Hi ! You can run the command if you download the repository\r\n> \r\n> ```\r\n> git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest\r\n> ```\r\n> \r\n> and run the command\r\n> \r\n> ```\r\n> datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py\r\n> ```\r\n> \r\n> (though on my side it doesn't manage to download the data since the dataset is private ^^)\r\n\r\nHi! Thanks for the info. \r\ngit cannot find the repository. Do you know if they have depreciated these tests and created a new one?",
"I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`",
"> I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`\r\n\r\nyour example repo and this page `https://huggingface.co/docs/datasets/add_dataset.html` helped me to solve.. thanks a lot"
] | 1,635,187,950,000 | 1,637,856,269,000 | null | NONE | null | It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).
I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!
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"@lhoestq \r\nI address all the comments, I think. Thanks! \r\n",
"The five test fails are unrelated to this PR and fixed on master so we can ignore them"
] | 1,635,186,656,000 | 1,636,034,475,000 | 1,636,034,475,000 | CONTRIBUTOR | null | Adding a new dataset for QA with riddles. I'm confused about the tagging process because it looks like the streamlit app loads data from the current repo, so is it something that should be done after merging or off my fork? | {
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https://api.github.com/repos/huggingface/datasets/issues/3160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3160/comments | https://api.github.com/repos/huggingface/datasets/issues/3160/events | https://github.com/huggingface/datasets/pull/3160 | 1,035,274,640 | PR_kwDODunzps4tofO0 | 3,160 | Better error msg if `len(predictions)` doesn't match `len(references)` in metrics | {
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"Can't test this now but it may be a good improvement indeed.",
"I added a function, but it only works with the `list` type. For arrays/tensors, we delegate formatting to the frameworks. "
] | 1,635,175,505,000 | 1,636,112,699,000 | 1,636,104,662,000 | CONTRIBUTOR | null | Improve the error message in `Metric.add_batch` if `len(predictions)` doesn't match `len(references)`.
cc: @BramVanroy (feel free to test this code on your examples and review this PR) | {
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"This PR is already working (although not very beautiful; see below): the idea was to have the `DatasetModule.builder_kwargs` accessible from the `builder_cls`, so that this can generate the default builder config (at the class level, without requiring the builder to be instantiated).\r\n\r\nI have a plan for a follow-up refactoring (same functionality, better implementation, much nicer), but I think we could already merge this, so that @severo can test it in the datasets previewer and report any potential issues.",
"Yes @lhoestq you are completely right. Indeed I was exclusively using `builder_cls.kwargs` to get the community dataset `name` (nothing else): \"lhoestq___demo1\"\r\n\r\nSee et: https://github.com/huggingface/datasets/pull/3159/files#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R413-R415\r\n\r\nIn your example, the `name` I was getting from `builder_cls.kwargs` was:\r\n```python\r\n{\"name\": \"lhoestq___demo1\",...}\r\n```\r\n\r\nI'm going to refactor all the approach... as I only need the name for this specific case ;)",
"I think this makes more sense now, @lhoestq @severo π
",
"It works well, thanks!"
] | 1,635,170,383,000 | 1,635,513,277,000 | 1,635,399,889,000 | MEMBER | null | Make all named configs cases, so that no special unnamed config case needs to be handled differently.
Fix #3135. | {
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https://api.github.com/repos/huggingface/datasets/issues/3158 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3158/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3158/comments | https://api.github.com/repos/huggingface/datasets/issues/3158/events | https://github.com/huggingface/datasets/pull/3158 | 1,035,158,070 | PR_kwDODunzps4toGpe | 3,158 | Fix string encoding for Value type | {
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"That was fast! \r\n"
] | 1,635,169,453,000 | 1,635,171,126,000 | 1,635,171,125,000 | MEMBER | null | Some metrics have `string` features but currently it fails if users pass integers instead. Indeed feature encoding that handles the conversion of the user's objects to the right python type is missing a case for `string`, while it already works as expected for integers, floats and booleans
Here is an example code that didn't work previously, but that works with this fix:
```python
import datasets
# Note that 'id' is an integer while the SQuAD metric uses strings
predictions = [{'prediction_text': '1976', 'id': 5}]
references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': 5}]
squad_metric = datasets.load_metric("squad")
squad_metric.add_batch(predictions=predictions, references=references)
results = squad_metric.compute()
# {'exact_match': 100.0, 'f1': 100.0}
```
cc @sgugger @philschmid | {
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https://api.github.com/repos/huggingface/datasets/issues/3157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3157/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3157/comments | https://api.github.com/repos/huggingface/datasets/issues/3157/events | https://github.com/huggingface/datasets/pull/3157 | 1,034,775,165 | PR_kwDODunzps4tm3_I | 3,157 | Fixed: duplicate parameter and missing parameter in docstring | {
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} | [] | closed | false | null | [] | null | [] | 1,635,146,760,000 | 1,635,170,539,000 | 1,635,170,539,000 | CONTRIBUTOR | null | changing duplicate parameter `data_files` in `DatasetBuilder.__init__` to the missing parameter `data_dir` | {
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https://api.github.com/repos/huggingface/datasets/issues/3156 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3156/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3156/comments | https://api.github.com/repos/huggingface/datasets/issues/3156/events | https://github.com/huggingface/datasets/issues/3156 | 1,034,478,844 | I_kwDODunzps49qOT8 | 3,156 | Rouge and Meteor for multiple references | {
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"Hi @avinashsai ,\r\n\r\ncurrently, multiple references are not supported. However, we could add a `multiref` config to fix that. When working with multiple references, we can accumulate them by either taking an average or the best score. Would you like to work on that?",
"@mariosasko I can help with this issue\r\n"
] | 1,635,098,931,000 | 1,639,289,786,000 | null | CONTRIBUTOR | null | Hi,
Currently rogue and meteor supports only single references. Can we use these metrics to calculate for multiple references? | {
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https://api.github.com/repos/huggingface/datasets/issues/3155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3155/comments | https://api.github.com/repos/huggingface/datasets/issues/3155/events | https://github.com/huggingface/datasets/issues/3155 | 1,034,468,757 | I_kwDODunzps49qL2V | 3,155 | Illegal instruction (core dumped) at datasets import | {
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"It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors."
] | 1,635,096,096,000 | 1,637,262,424,000 | 1,637,262,423,000 | CONTRIBUTOR | null | ## Describe the bug
I install datasets using conda and when I import datasets I get: "Illegal instruction (core dumped)"
## Steps to reproduce the bug
```
conda create --prefix path/to/env
conda activate path/to/env
conda install -c huggingface -c conda-forge datasets
# exits with output "Illegal instruction (core dumped)"
python -m datasets
```
## Environment info
When I run "datasets-cli env", I also get "Illegal instruction (core dumped)"
If I run the following commands:
```
conda create --prefix path/to/another/new/env
conda activate path/to/another/new/env
conda install -c huggingface transformers
transformers-cli env
```
Then I get:
- `transformers` version: 4.11.3
- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
Let me know what additional information you need in order to debug this issue. Thanks in advance! | {
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https://api.github.com/repos/huggingface/datasets/issues/3154 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3154/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3154/comments | https://api.github.com/repos/huggingface/datasets/issues/3154/events | https://github.com/huggingface/datasets/issues/3154 | 1,034,361,806 | I_kwDODunzps49pxvO | 3,154 | Sacrebleu unexpected behaviour/requirement for data format | {
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"Hi @BramVanroy!\r\n\r\nGood question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.\r\n\r\nThat's why your example throws an error even though it matches the schema:\r\n```python\r\nrefs = [\r\n ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n] # len(refs) = 2\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nInstead, it should be:\r\n```python\r\nrefs = [\r\n ['The dog bit the man.', 'The dog had bit the man.'],\r\n ['It was not unexpected.', 'No one was surprised.'],\r\n ['The man bit him first.', 'The man had bitten the dog.'], \r\n] # len(refs) = 3\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nHowever, `sacreblue` works with the format that's described in your example, hence this part:\r\nhttps://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99\r\n\r\nHope you get an idea!",
"Thanks, that makes sense. It is a bit unfortunate because it may be confusing to users since the input format is suddenly different than what they may expect from the underlying library/metric. But it is understandable due to how `datasets` works!"
] | 1,635,065,733,000 | 1,635,671,312,000 | 1,635,671,311,000 | CONTRIBUTOR | null | ## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3153/comments | https://api.github.com/repos/huggingface/datasets/issues/3153/events | https://github.com/huggingface/datasets/pull/3153 | 1,034,179,198 | PR_kwDODunzps4tlEVE | 3,153 | Add TER (as implemented in sacrebleu) | {
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"The problem appears to stem from the omission of the lines that you mentioned. If you add them back and try examples from [this](https://huggingface.co/docs/datasets/using_metrics.html) tutorial (sacrebleu metric example) the code you implemented works fine.\r\n\r\nI think the purpose of these lines is follows:\r\n\r\n1. Sacrebleu metrics confusingly expect a nested list of strings when you have just one reference for each hypothesis (i.e. `[[\"example1\", \"example2\", \"example3]]`), while for cases with more than one reference a _nested list of lists of strings_ (i.e. `[[\"ref1a\", \"ref1b\"], [\"ref2a\", \"ref2b\"], [\"ref3a\", \"ref3b\"]]`) is expected instead. So `transformed_references` line outputs the required single reference format for sacrebleu's ter implementation which you can't pass directly to `compute`.\r\n2. I'm assuming that an additional check is also related to that confusing format with one/many references, because it's really difficult to tell what exactly you're doing wrong if you're not aware of that issue."
] | 1,634,999,205,000 | 1,635,851,051,000 | 1,635,851,051,000 | CONTRIBUTOR | null | Implements TER (Translation Edit Rate) as per its implementation in sacrebleu. Sacrebleu for BLEU scores is already implemented in `datasets` so I thought this would be a nice addition.
I started from the sacrebleu implementation, as the two metrics have a lot in common.
Verified with sacrebleu's [testing suite](https://github.com/mjpost/sacrebleu/blob/078c440168c6adc89ba75fe6d63f0d922d42bcfe/test/test_ter.py) that this indeed works as intended.
```python
import datasets
test_cases = [
(['aaaa bbbb cccc dddd'], ['aaaa bbbb cccc dddd'], 0), # perfect match
(['dddd eeee ffff'], ['aaaa bbbb cccc'], 1), # no overlap
([''], ['a'], 1), # corner case, empty hypothesis
(['d e f g h a b c'], ['a b c d e f g h'], 1 / 8), # a single shift fixes MT
(
[
'wΓ€hlen Sie " Bild neu berechnen , " um beim Γndern der BildgrΓΆΓe Pixel hinzuzufΓΌgen oder zu entfernen , damit das Bild ungefΓ€hr dieselbe GrΓΆΓe aufweist wie die andere GrΓΆΓe .',
'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren mΓΆchten , wΓ€hlen Sie im MenΓΌ des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "',
'klicken Sie auf der Registerkarte " Optionen " auf die SchaltflΓ€che " Benutzerdefiniert " und geben Sie Werte fΓΌr " Fehlerkorrektur-Level " und " Y / X-VerhΓ€ltnis " ein .',
'Sie kΓΆnnen beispielsweise ein Dokument erstellen , das ein Auto ΓΌber die BΓΌhne enthΓ€lt .',
'wΓ€hlen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "',
],
[
'wΓ€hlen Sie " Bild neu berechnen , " um beim Γndern der BildgrΓΆΓe Pixel hinzuzufΓΌgen oder zu entfernen , damit die Darstellung des Bildes in einer anderen GrΓΆΓe beibehalten wird .',
'wenn Sie alle Aufgaben im aktuellen Dokument aktualisieren mΓΆchten , wΓ€hlen Sie im MenΓΌ des Aufgabenbedienfelds die Option " Alle Aufgaben aktualisieren . "',
'klicken Sie auf der Registerkarte " Optionen " auf die SchaltflΓ€che " Benutzerdefiniert " und geben Sie fΓΌr " Fehlerkorrektur-Level " und " Y / X-VerhΓ€ltnis " niedrigere Werte ein .',
'Sie kΓΆnnen beispielsweise ein Dokument erstellen , das ein Auto enthalt , das sich ΓΌber die BΓΌhne bewegt .',
'wΓ€hlen Sie im Dialogfeld " Neu aus Vorlage " eine Vorlage aus und klicken Sie auf " Neu . "',
],
0.136 # realistic example from WMT dev data (2019)
),
]
ter = datasets.load_metric(r"path\to\datasets\metrics\ter")
predictions = ["hello there general kenobi", "foo bar foobar"]
references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
print(ter.compute(predictions=predictions, references=references))
for hyp, ref, score in test_cases:
# Note the reference transformation which is different from scarebleu's input format
results = ter.compute(predictions=hyp, references=[[r] for r in ref])
assert 100*score == results["score"], f"expected {100*score}, got {results['score']}"
```
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https://api.github.com/repos/huggingface/datasets/issues/3152 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3152/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3152/comments | https://api.github.com/repos/huggingface/datasets/issues/3152/events | https://github.com/huggingface/datasets/pull/3152 | 1,034,039,379 | PR_kwDODunzps4tkqi- | 3,152 | Fix some typos in the documentation | {
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https://api.github.com/repos/huggingface/datasets/issues/3151 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3151/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3151/comments | https://api.github.com/repos/huggingface/datasets/issues/3151/events | https://github.com/huggingface/datasets/pull/3151 | 1,033,890,501 | PR_kwDODunzps4tkL7t | 3,151 | Re-add faiss to windows testing suite | {
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} | [] | closed | false | null | [] | null | [] | 1,634,931,269,000 | 1,635,850,054,000 | 1,635,847,563,000 | CONTRIBUTOR | null | In recent versions, `faiss-cpu` seems to be available for Windows as well. See the [PyPi page](https://pypi.org/project/faiss-cpu/#files) to confirm. We can therefore included it for Windows in the setup file.
At first tests didn't pass due to problems with permissions as caused by `NamedTemporaryFile` on Windows. This built-in library is notoriously poor in playing nice on Windows. The required change isn't pretty, but it works. First set `delete=False` to not automatically try to delete the file on `exit`. Then, manually delete the file with `unlink`. It's weird, I know, but it works.
```python
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
# do stuff
os.unlink(tmp_file.name)
```
closes #3150 | {
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"Sure, feel free to open a PR."
] | 1,634,926,036,000 | 1,635,847,563,000 | 1,635,847,563,000 | CONTRIBUTOR | null | In the setup file, I find the following:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/setup.py#L171
However, FAISS does install perfectly fine on Windows on my system. You can also confirm this on the [PyPi page](https://pypi.org/project/faiss-cpu/#files), where Windows wheels are available. Maybe this was true for older versions? For current versions, this can be removed I think.
(This isn't really a bug but didn't know how else to tag.)
If you agree I can do a quick PR and remove that line. | {
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"Hi @lhoestq, thanks a lot for the help. I have moved the part as suggested. \r\nAlthough still while running the dummy data script, I face this issue\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/ishan/anaconda3/bin/datasets-cli\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 318, in run\r\n self._autogenerate_dummy_data(\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/commands/dummy_data.py\", line 363, in _autogenerate_dummy_data\r\n dataset_builder._prepare_split(split_generator)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/builder.py\", line 1103, in _prepare_split\r\n example = self.info.features.encode_example(record)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 981, in encode_example\r\n return encode_nested_example(self, example)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in encode_nested_example\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/features/features.py\", line 775, in <dictcomp>\r\n return {\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in zip_dict\r\n yield key, tuple(d[key] for d in dicts)\r\n File \"/home/ishan/anaconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 99, in <genexpr>\r\n yield key, tuple(d[key] for d in dicts)\r\nKeyError: 'status'\r\n```\r\nThis KeyError is at times different from 'status' also.\r\nwhen I run \r\n```\r\ndatasets-cli dummy_data datasets/cmu_hinglish_dog --auto_generate --json_field='history'\r\n```\r\nI have tried removing unnecessary feature type definition, but that didn't help. Please let me know if I am missing something, thanks!",
"The CI fail is unrelated to this PR and fixed on master. Merging !"
] | 1,634,919,445,000 | 1,636,976,202,000 | 1,636,972,065,000 | CONTRIBUTOR | null | Address part of #2841
Added the CMU Hinglish DoG Dataset as in GLUECoS. Added it as a seperate dataset as unlike other tasks of GLUE CoS this can't be evaluated for a BERT like model.
Consists of parallel dataset between Hinglish (Hindi-English) and English, can be used for Machine Translation between the two.
The data processing part is inspired from the GLUECoS repo [here](https://github.com/microsoft/GLUECoS/blob/7fdc51653e37a32aee17505c47b7d1da364fa77e/Data/Preprocess_Scripts/preprocess_mt_en_hi.py)
The dummy data part is not working properly, it shows
``` UnboundLocalError: local variable 'generator_splits' referenced before assignment ```
when I run without ``--auto_generate``.
Please let me know how I can fix that.
Thanks | {
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"I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. "
] | 1,634,915,237,000 | 1,640,664,745,000 | null | NONE | null | ## Describe the bug
When using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.
The code owner is likely @lhoestq
## Steps to reproduce the bug
For your convenience, we've prepped a colab notebook that reproduces the bug
https://colab.research.google.com/drive/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing
```python
!pip install datasets==1.14.0
should_freeze_forever = True
# ^-- set this to True in order to freeze forever, set to False in order to work normally
import torch
from datasets import load_dataset
data = load_dataset("oscar", "unshuffled_deduplicated_bn", split="train", streaming=True)
data = data.map(lambda x: {"text": x["text"], "orig": f"oscar[{x['id']}]"}, batched=True)
data = data.shuffle(100, seed=1337)
data = data.with_format("torch")
loader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)
# v-- the code should freeze forever at this line
for i, row in enumerate(loader):
print(row)
if i > 10: break
print("DONE!")
```
## Expected results
The code should not freeze forever with num_workers=2
## Actual results
The code freezes forever with num_workers=2
## Environment info
- `datasets` version: 1.14.0 (also found in previous versions)
- Platform: google colab (also locally)
- Python version: 3.7, (also 3.8)
- PyArrow version: 3.0.0
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] | null | [] | 1,634,910,653,000 | 1,635,321,709,000 | 1,635,321,709,000 | MEMBER | null | When trying to generate a datset JSON metadata, a `NonMatchingSplitsSizesError` is thrown:
```
$ datasets-cli test datasets/arabic_billion_words --save_infos --all_configs
Testing builder 'Alittihad' (1/10)
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: Unknown size, post-processed: Unknown size, total: 332.13 MiB) to .cache\arabic_billion_words\Alittihad\1.1.0\8175ff1c9714c6d5d15b1141b6042e5edf048276bb81a9c14e35e149a7a62ae4...
Traceback (most recent call last):
File "path\huggingface\datasets\.venv\Scripts\datasets-cli-script.py", line 33, in <module>
sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())
File "path\huggingface\datasets\src\datasets\commands\datasets_cli.py", line 33, in main
service.run()
File "path\huggingface\datasets\src\datasets\commands\test.py", line 144, in run
builder.download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "path\huggingface\datasets\src\datasets\builder.py", line 709, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "path\huggingface\datasets\src\datasets\utils\info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=1601790302, num_examples=349342, dataset_name='arabic_billion_words')}]
```
This is due because a previous run generated a wrong `dataset_info.json`.
This error can be avoided by passing `--ignore_verifications`, but I think this should be assumed when passing `--save_infos`. | {
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"@severo, maybe somehow related to this PR ?\r\n- #3129",
"@severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage). ",
"Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all",
"Fixed with https://github.com/huggingface/datasets/pull/3163"
] | 1,634,909,029,000 | 1,640,171,137,000 | 1,640,171,136,000 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
When a dataset cell contains a value of type Image (be it from a remote URL, an Array2D/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.
Note: this issue would apply exactly the same for the `Audio` type.
**Describe the solution you'd like**
If a "cell" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:
```python
filename: str
data: bytes
```
**Describe alternatives you've considered**
A way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.
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Some datasets, in particular community datasets, have no info file, thus no features.
**Describe the solution you'd like**
If a dataset has no features, the first loaded data (5-10 rows) could be used to infer the type.
Related: `datasets` would provide a way to load the data, and get the rows AND the features as the result.
**Describe alternatives you've considered**
The HF hub could also provide some UI to help the dataset maintainers to explicit the types of their rows, or automatically infer them as an initial proposal. | {
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"Related: #3144 "
] | 1,634,908,416,000 | 1,634,908,676,000 | null | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
I understand that currently the data loaded has not always the type described in the info features
**Describe the solution you'd like**
Provide a way to check if the rows have the type described by info features
**Describe alternatives you've considered**
Always check it, and raise an error when loading the data if their type doesn't match the features.
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"Yes, I agree this feature is much needed. We could do something similar to what TF does (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache). \r\n\r\nIdeally, if the entire streamed dataset is consumed/cached, the generated cache should be reusable for the Arrow dataset."
] | 1,634,908,193,000 | 1,635,506,079,000 | null | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
The streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.
**Describe the solution you'd like**
Provide a way to write the streamed rows of a dataset on the disk, and to load from it later.
**Describe alternatives you've considered**
Provide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data.
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* remove ")" added by accident in the dataset dir name
* correctly pass the namespace kwargs in `CachedDatasetModuleFactory`
* improve the warning message if `HF_DATASETS_OFFLINE is `True`
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This is used by SUPERB beenchmark, for example. | {
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Examples:
- download a dataset, then force redownload it in the same session while keeping a reference to the downloaded dataset
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset = load_dataset("sst", split="train", download_mode="force_redownload")
```
- try to clean up the cache files while keeping a reference to those files (via the mapped dataset):
```python
from datasets import load_dataset
dset = load_dataset("sst", split="train")
dset_mapped = dset.map(lambda _: {"dummy_col": 1})
dset.cleanup_cache_files()
```
We should fix those.
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Exceptions like `FileNotFoundError` can be raised by different parts of the code, and it's hard to detect which one did
**Describe the solution you'd like**
Give a specific exception type for every group of similar errors
**Describe alternatives you've considered**
Rely on the error message, using regex
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"This'll be a really helpful fix, thank you!"
] | 1,634,894,266,000 | 1,634,918,655,000 | 1,634,918,654,000 | MEMBER | null | Numpy shows a deprecation warning when we call `np.array` on a list of ragged tensors without specifying the `dtype`. If their shapes match, the tensors can be collated together, otherwise the resulting array should have `dtype=np.object`.
Fix #3084
cc @Rocketknight1 | {
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This PR fixes the loading script so that is properly parses the entire dataset.
Current implementation generates the same number of examples as reported in the [original paper](https://arxiv.org/abs/1611.04033) for all configurations except for one:
- For "Youm7" we generate more examples (1172136) than the ones reported by the paper (1025027)
| | Number of examples | Number of examples according to the source |
|:---------------|-------------------:|-----:|
| Alittihad | 349342 |349342 |
| Almasryalyoum | 291723 |291723 |
| Almustaqbal | 446873 |446873 |
| Alqabas | 817274 |817274 |
| Echoroukonline | 139732 |139732 |
| Ryiadh | 858188 | 858188 |
| Sabanews | 92149 |92149 |
| SaudiYoum | 888068 |888068 |
| Techreen | 314597 |314597 |
| Youm7 | 1172136 |1025027 |
Fix #3126. | {
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https://api.github.com/repos/huggingface/datasets/issues/3135 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3135/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3135/comments | https://api.github.com/repos/huggingface/datasets/issues/3135/events | https://github.com/huggingface/datasets/issues/3135 | 1,033,294,299 | I_kwDODunzps49ltHb | 3,135 | Make inspect.get_dataset_config_names always return a non-empty list of configs | {
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"Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?",
"Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check/region_1`)"
] | 1,634,889,770,000 | 1,635,399,889,000 | 1,635,399,889,000 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
Currently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to
**Describe the solution you'd like**
In that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check/region_1`).
https://github.com/huggingface/datasets/blob/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af/src/datasets/inspect.py#L161
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https://api.github.com/repos/huggingface/datasets/issues/3134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3134/comments | https://api.github.com/repos/huggingface/datasets/issues/3134/events | https://github.com/huggingface/datasets/issues/3134 | 1,033,251,755 | I_kwDODunzps49liur | 3,134 | Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py | {
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"Hi,\r\n\r\nDid you try to run the code multiple times (GitHub URLs can be down sometimes for various reasons)? I can access `https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py`, so this code is working without an error on my side. \r\n\r\nAdditionally, can you please run the `datasets-cli env` command because it seems to me that you are using the `datasets` version different from `1.12.1`?",
"Same issue when running `metric = datasets.load_metric(\"accuracy\")`.\r\nError info is:\r\n```\r\nmetric = datasets.load_metric(\"accuracy\")\r\nTraceback (most recent call last):\r\n\r\n File \"<ipython-input-2-d25db38b26c5>\", line 1, in <module>\r\n metric = datasets.load_metric(\"accuracy\")\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 610, in load_metric\r\n module_path, _ = prepare_module(\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 330, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 288, in cached_path\r\n output_path = get_from_cache(\r\n\r\n File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 605, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py\r\n```\r\n\r\n\r\n My `datasets-cli env` result is as follows:\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n@yananchen1989 did you find a way to solve this?",
"It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. \r\nchange `metric = datasets.load_metric(\"accuracy\")` to `metric = datasets.load_metric(path = \"./accuracy.py\")`.\r\nCopy `accuracy.py` from browser at [accuracy.py](https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/accuracy/accuracy.py)"
] | 1,634,886,472,000 | 1,635,765,469,000 | null | NONE | null | datasets version: 1.12.1
`metric = datasets.load_metric('rouge')`
The error:
> ConnectionError Traceback (most recent call last)
> <ipython-input-3-dd10a0c5212f> in <module>
> ----> 1 metric = datasets.load_metric('rouge')
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
> 613 download_config=download_config,
> 614 download_mode=download_mode,
> --> 615 dataset=False,
> 616 )
> 617 metric_cls = import_main_class(module_path, dataset=False)
>
> /usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
> 328 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
> 329 try:
> --> 330 local_path = cached_path(file_path, download_config=download_config)
> 331 except FileNotFoundError:
> 332 if script_version is not None:
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
> 296 use_etag=download_config.use_etag,
> 297 max_retries=download_config.max_retries,
> --> 298 use_auth_token=download_config.use_auth_token,
> 299 )
> 300 elif os.path.exists(url_or_filename):
>
> /usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
> 603 raise FileNotFoundError("Couldn't find file at {}".format(url))
> 604 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
> --> 605 raise ConnectionError("Couldn't reach {}".format(url))
> 606
> 607 # Try a second time
>
> ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.11.0/metrics/rouge/rouge.py
Is there any remedy to solve the connection issue ? | {
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Due to the large size of many speech datasets, we should also support Audio feature in streaming mode.
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] | open | false | null | [] | null | [] | 1,634,811,189,000 | 1,638,964,860,000 | null | NONE | null | ## Adding a Dataset
- **Name:** ADE20k (actually it's called the MIT Scene Parsing Benchmark, it's actually a subset of ADE20k but a lot of authors still call it ADE20k)
- **Description:** A semantic segmentation dataset, consisting of 150 classes.
- **Paper:** http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf
- **Data:** http://sceneparsing.csail.mit.edu/
- **Motivation:** I am currently adding Transformer-based semantic segmentation models that achieve SOTA on this dataset. It would be great to directly access this dataset using HuggingFace Datasets, in order to make example scripts in HuggingFace Transformers.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"Hi @zidingz, thanks for your contribution.\r\n\r\nHowever I am closing it because it is a duplicate of a previous PR:\r\n - #2958\r\n\r\n"
] | 1,634,810,583,000 | 1,634,826,808,000 | 1,634,826,710,000 | NONE | null | To let the repository confirm feedback@huggingface.co as its security contact. | {
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"Also do you think we can adapt `cast_column` to keep the same value for this new parameter when the user only wants to change the sampling rate ?",
"Thanks for your comments, @lhoestq, I will address them afterwards.\r\n\r\nBut, I think it is more important/urgent first address the current blocking non-passing test: https://github.com/huggingface/datasets/runs/4143579241?check_suite_focus=true\r\n- I am thinking of a way of solving it, but if you have any hint, it will be more than welcome! π
\r\n\r\nBasically:\r\n```\r\n{'audio': '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_dataset_with_audio_featur1/data/test_audio_44100.wav'}\r\n``` \r\nbecomes\r\n```\r\n{'audio': {'bytes': None, 'path': '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_dataset_with_audio_featur1/data/test_audio_44100.wav'}}\r\n```\r\nafter a `map`, which is what was stored in the Arrow file. However we expect it remains invariant after this `map`.",
"@lhoestq, @mariosasko I finally proposed another implementation different from my last one:\r\n- Before: store Audio always a struct<path: string, bytes: binary>, where bytes can be None\r\n- Now, depending on the examples, either store Audio as a struct (as before), or as a string.\r\n\r\nPlease note that the main motivation for this change was the issue mentioned above: https://github.com/huggingface/datasets/pull/3129#issuecomment-964347056\r\n",
"Until here we had the assumption that a Features object always has an associated, deterministic, pyarrow schema. This is useful to ensure that we are able to concatenate two datasets that have the same features for example.\r\n\r\nBy breaking this assumption for the Audio type, how can we ensure that we can concatenate two audio datasets if one has Audio as a struct and the other a string ?",
"Oh I noticed that the Audio feature type has a private attribute `_storage_dtype`, so the assumption still holds, since they are now different feature types depending on the this attribute :)\r\n(i mean different from the python equal operator point of view)",
"I think this PR is ready, @lhoestq, @mariosasko. ",
"Nit: We should also mention the new storage structure in the `Features` docstring [here](https://github.com/huggingface/datasets/blob/b29fb550c31de337b952035a7584147e0f18c0cf/src/datasets/features/features.py#L966) for users to know what type of value to return in their dataset scripts (we also have a link to that docstring in the `ADD_NEW_DATASET` template)."
] | 1,634,806,611,000 | 1,637,170,928,000 | 1,637,170,927,000 | MEMBER | null | Add Audio feature support for TAR archived files in sequential access.
Fix #3128. | {
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However, streamed TAR archive files do not allow random access to their archived files.
Therefore, we should enhance the Audio feature to support TAR archived files in sequential access. | {
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"Hi,\r\n\r\nthe MNIST dataset is already available on the Hub. You can use it as follows:\r\n```python\r\nimport datasets\r\ndataset_dict = datasets.load_dataset(\"mnist\")\r\n```\r\n\r\nAs for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned."
] | 1,634,796,867,000 | 1,635,334,565,000 | null | NONE | null | ### Discussed in https://github.com/huggingface/datasets/discussions/3079
<div type='discussions-op-text'>
<sup>Originally posted by **vitalyshalumov** October 14, 2021</sup>
I'm trying to convert a tfds dataset to a huggingface one.
I've tried:
1. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/mnist/3.0.1/
2. datasets-cli convert --tfds_path ~/tensorflow_datasets/mnist/3.0.1/ --datasets_directory ~/.cache/huggingface/datasets/
and other permutations.
The script appears to be running and finishing without an error but when looking in the huggingface/datasets/ folder nothing is created.
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"Thanks for reporting, @vitalyshalumov.\r\n\r\nApparently the script to parse the data has a bug, and does not generate the entire dataset.\r\n\r\nI'm fixing it."
] | 1,634,796,158,000 | 1,634,909,320,000 | 1,634,909,320,000 | NONE | null | ## Describe the bug
When running:
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
the correct dataset file is pulled from the url.
But, the generated dataset includes just a small portion of the data included in the file.
This is true for all other portions of the "arabic_billion_words" dataset ('Almasryalyoum',.....)
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
raw_dataset = load_dataset('arabic_billion_words','Alittihad')
#The screen message
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB)
## Expected results
over 100K sentences
## Actual results
only 11K sentences
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3125 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3125/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3125/comments | https://api.github.com/repos/huggingface/datasets/issues/3125/events | https://github.com/huggingface/datasets/pull/3125 | 1,032,046,666 | PR_kwDODunzps4teNPC | 3,125 | Add SLR83 to OpenSLR | {
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https://api.github.com/repos/huggingface/datasets/issues/3124 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3124/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3124/comments | https://api.github.com/repos/huggingface/datasets/issues/3124/events | https://github.com/huggingface/datasets/pull/3124 | 1,031,976,286 | PR_kwDODunzps4td-5w | 3,124 | More efficient nested features encoding | {
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"@lhoestq @albertvillanova @mariosasko\r\nCan you please check this out?",
"Thanks, done!"
] | 1,634,781,331,000 | 1,635,865,633,000 | 1,635,851,044,000 | CONTRIBUTOR | null | Nested encoding of features wastes a lot of time on operations which are effectively doing nothing when lists are used.
For example, if in the input we have a list of integers, `encoded_nested_example` will iterate over it and apply `encoded_nested_example` on every element even though it just return the int as is.
A similar issue is handled at an earlier stage when casting pytorch/tensorflow/pandas objects to python lists/numpy arrays:
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L149-L156
https://github.com/huggingface/datasets/blob/c98c23c4260edadab00f997d1a5d66b7f2e93ce9/src/datasets/features/features.py#L212-L228
In this pull request I suggest to use the same approach in `encoded_nested_example`.
In my setup there was a major speedup with this change: loading the data was at least x4 faster. | {
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https://api.github.com/repos/huggingface/datasets/issues/3123 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3123/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3123/comments | https://api.github.com/repos/huggingface/datasets/issues/3123/events | https://github.com/huggingface/datasets/issues/3123 | 1,031,793,207 | I_kwDODunzps49f-o3 | 3,123 | Segmentation fault when loading datasets from file | {
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"Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example\r\n\r\nhttps://issues.apache.org/jira/browse/ARROW-14439\r\n\r\n```python\r\nimport io\r\n\r\nimport pyarrow.json as paj\r\n\r\nbatch = b'{\"a\": [], \"b\": 1}\\n{\"b\": 1}'\r\nblock_size = 12\r\n\r\npaj.read_json(\r\n io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n)\r\n```\r\n\r\nI don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though",
"The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)\r\n\r\nThe issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists"
] | 1,634,760,971,000 | 1,635,865,027,000 | 1,635,865,027,000 | MEMBER | null | ## Describe the bug
Custom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features/
## Steps to reproduce the bug
Download an example file:
```
wget https://gist.githubusercontent.com/TevenLeScao/11e2184394b3fa47d693de2550942c6b/raw/4232704d08fbfcaf93e5b51def9e5051507651ad/tiny_kelm.jsonl
```
Then in Python:
```
import datasets
tiny_kelm = datasets.load_dataset("json", data_files="tiny_kelm.jsonl", chunksize=100000)
```
## Expected results
a `tiny_kelm` functional dataset
## Actual results
β οΈ `Segmentation fault (core dumped)` β οΈ
## Environment info
- `datasets` version: 1.14.0
- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3122 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3122/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3122/comments | https://api.github.com/repos/huggingface/datasets/issues/3122/events | https://github.com/huggingface/datasets/issues/3122 | 1,031,787,509 | I_kwDODunzps49f9P1 | 3,122 | OSError with a custom dataset loading script | {
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"Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla/janes_tag` and the `classla/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data` -> data files).\r\n\r\nThis can be fixed by removing the `os.path.join` call in https://huggingface.co/datasets/classla/janes_tag/blob/main/janes_tag.py#L86\r\n\r\nLet me know if this works for you.",
"Hi Mario,\r\n\r\nI had already tried that before, but it didn't work. I have now recreated the `classla/janes_tag` zip file so that it also contains the `data` directory, but I am still getting the same error.",
"Hi,\r\n\r\nI just tried to download the `classla/janes_tag` dataset, and this time the zip file is extracted correctly. However, the script is now throwing the IndexError, probably due to a bug in the `_generate_examples`.\r\n\r\nLet me know if you are still getting the same error.",
"I am still getting the same error.",
"Hi, \r\n\r\ncould you try to download the dataset with a different `cache_dir` like so:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('classla/janes_tag', split='validation', cache_dir=\"path/to/different/cache/dir\")\r\n```\r\nIf this works, then most likely the cached extracted data is causing issues. This data is stored at `~/.cache/huggingface/datasets/downloads/extracted` and needs to be deleted, and then it should work (you can easily locate the directory with the path given in the `OSError` message). Additionally, I'd suggest you to update `datasets` to the newest version with:\r\n```\r\npip install -U datasets\r\n```",
"Thank you, deleting the `~/.cache/huggingface/datasets/downloads/extracted` directory helped. However, I am still having problems.\r\n\r\nThere was indeed a bug in the script that was throwing an `IndexError`, which I have now corrected (added the condition to skip the lines starting with '# text') and it is working locally, but still throws an error when I try to load the dataset from HuggingFace. I literally copied and pasted the `_generate_examples` function and ran it on the `dev_all.conllup` file, which I even re-downloaded from the repository to be certain that the files are exactly the same. I also deleted everything again just in case, but it didn't help. The code works locally, but throws an `IndexError` when loading from `datasets.`",
"Hi,\r\n\r\nDid some investigation.\r\n\r\nTo fix the dataset script on the Hub, append the following labels to the `names` list of the `upos_tags` field:\r\n```'INTJ NOUN', 'AUX PRON', 'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'```.\r\n\r\nThis step is required to avoid an error due to missing labels in the following step which is:\r\n```python\r\nload_dataset(\"classla/janes_tag\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\nThis will generate and cache the dataset, so specifying `download_mode` will not be required anymore unless you update the script/data on the Hub.",
"It works now, thank you!"
] | 1,634,760,519,000 | 1,637,661,338,000 | 1,637,661,338,000 | NONE | null | ## Describe the bug
I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('classla/janes_tag', split='validation')
```
## Expected results
Dataset correctly loaded.
## Actual results
Traceback (most recent call last):
File "C:/mypath/test.py", line 91, in <module>
load_and_print('janes_tag')
File "C:/mypath/test.py", line 32, in load_and_print
dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation')
File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.5
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3121 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3121/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3121/comments | https://api.github.com/repos/huggingface/datasets/issues/3121/events | https://github.com/huggingface/datasets/pull/3121 | 1,031,673,115 | PR_kwDODunzps4tc_6q | 3,121 | Use huggingface_hub.HfApi to list datasets/metrics | {
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WIP until https://github.com/huggingface/huggingface_hub/pull/429 is merged, then wait for the new release of `huggingface_hub`, update the `huggingface_hub` version in `setup.py` and merge this PR.
cc: @lhoestq | {
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} | [] | closed | false | null | [] | null | [] | 1,634,745,298,000 | 1,634,891,331,000 | 1,634,827,821,000 | CONTRIBUTOR | null | This PR correctly updates metadata to preserve higher-level feature types (e.g. `ClassLabel`) in `datasets.concatenate_datasets` when `axis=1`. Previously, we would delete the feature metadata in `datasets.concatenate_datasets` if `axis=1` and restore the feature types from the arrow table schema in `Dataset.__init__`. However, this approach only works for simple feature types (e.g. `Value`).
Fixes #3111 | {
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https://api.github.com/repos/huggingface/datasets/issues/3119 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3119/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3119/comments | https://api.github.com/repos/huggingface/datasets/issues/3119/events | https://github.com/huggingface/datasets/issues/3119 | 1,031,328,044 | I_kwDODunzps49eNEs | 3,119 | Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech | {
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"Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files."
] | 1,634,731,507,000 | 1,634,929,252,000 | 1,634,891,422,000 | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** *openslr**
- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*
- **Paper:** *https://www.openslr.org/resources/83/about.html*
- **Data:** *Eleven separate data files can be found via https://www.openslr.org/resources/83/*
- **Motivation:** *Increase english ASR data with UK and Irish dialects*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
The *openslr* dataset already exists, this will add additional subset, *SLR83*. | {
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} | [] | closed | false | null | [] | null | [] | 1,634,730,278,000 | 1,634,734,956,000 | 1,634,734,956,000 | MEMBER | null | Fix test_load_dataset_canonical at release commit.
Fix #3117. | {
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] | null | [] | 1,634,730,173,000 | 1,634,734,955,000 | 1,634,734,955,000 | MEMBER | null | After 1.12.0, there is a recurrent CI error at each release commit: https://app.circleci.com/pipelines/github/huggingface/datasets/8289/workflows/665d954d-e409-4602-8202-e678594d2946/jobs/51110
```
____________________ LoadTest.test_load_dataset_canonical _____________________
[gw0] win32 -- Python 3.6.8 C:\tools\miniconda3\python.exe
self = <tests.test_load.LoadTest testMethod=test_load_dataset_canonical>
def test_load_dataset_canonical(self):
scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
with self.assertRaises(FileNotFoundError) as context:
datasets.load_dataset("_dummy")
self.assertIn(
f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
> str(context.exception),
)
E AssertionError: 'https://raw.githubusercontent.com/huggingface/datasets/1.14.0/datasets/_dummy/_dummy.py' not found in "Couldn't find a dataset script at C:\\Users\\circleci\\datasets\\_dummy\\_dummy.py or any data file in the same directory. Couldn't find '_dummy' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py"
tests\test_load.py:358: AssertionError
```
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* updates the README links and the ADD_NEW_DATASET template to point to the new docs (the new docs don't have a section with the list of all the possible features, so I added that info to the `Features` docstring, which is then referenced in the ADD_NEW_DATASET template)
* fixes some broken links in the `.rst` files (fixed with the `make linkcheck` tool) | {
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"Hi ! Can you try again with pyarrow 6.0.0 ? I think it includes some changes regarding filesystems compatibility with fsspec.",
"Hi @lhoestq! I ended up using `fsspec.implementations.arrow.HadoopFileSystem` which doesn't have the problem I described with pyarrow 5.0.0.\r\n\r\nI'll try again with `PyArrowHDFS` once I update arrow to 6.0.0.\r\n\r\nThanks!"
] | 1,634,673,705,000 | 1,637,282,123,000 | null | CONTRIBUTOR | null | ## Describe the bug
Passing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.
## Steps to reproduce the bug
The documentation for the `fs` parameter states:
```
fs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):
Instance of the remote filesystem used to download the files from.
```
`PyArrowHDFS` from [fsspec](https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.
```python
from fsspec.implementations.hdfs import PyArrowHDFS
...
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
```
## Expected results
Previous to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:
```python
transformed_corpus_path = "/user/my_user/clickbait/transformed_ds/"
fs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)
my_datasets.save_to_disk(transformed_corpus_path, fs=fs)
```
As I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:
```sh
$ hadoop fs -ls "/user/my_user/clickbait/transformed_ds/"
Found 4 items
-rw------- 3 my_user users 43 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/dataset_dict.json
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/test
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/train
drwx------ - my_user users 0 2021-10-19 03:08 /user/my_user/clickbait/transformed_ds/validation
```
I would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above.
## Actual results
However, when trying to recover the saved datasets, I get this error:
```
...
File "/home/fperez/dev/neuromancer/neuromancer/corpus.py", line 186, in load_transformed_corpus_from_disk
dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/dataset_dict.py", line 748, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
File "/home/fperez/anaconda3/envs/neuromancer/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1048, in load_from_disk
fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)
File "pyarrow/_hdfsio.pyx", line 438, in pyarrow._hdfsio.HadoopFileSystem.download
TypeError: download() got an unexpected keyword argument 'recursive'
```
Examining the [signature of the download method in pyarrow 5.0.0](https://github.com/apache/arrow/blob/54d2bd89c99df72fa091b025452f85dd5d88e3cf/python/pyarrow/_hdfsio.pyx#L438) we can see that there's no download parameter:
```python
def download(self, path, stream, buffer_size=None):
with self.open(path, 'rb') as f:
f.download(stream, buffer_size=buffer_size)
```
## Environment info
- `datasets` version: 1.13.3
- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3113 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3113/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3113/comments | https://api.github.com/repos/huggingface/datasets/issues/3113/events | https://github.com/huggingface/datasets/issues/3113 | 1,030,667,547 | I_kwDODunzps49br0b | 3,113 | Loading Data from HDF files | {
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] | null | [] | 1,634,671,606,000 | 1,634,672,568,000 | null | NONE | null | **Is your feature request related to a problem? Please describe.**
More often than not I come along big HDF datasets, and currently there is no straight forward way to feed them to a dataset.
**Describe the solution you'd like**
I would love to see a `from_h5` method that gets an interface implemented by the user on how items are extracted from dataset (in case of multiple datasets containing elements like arrays and metadata and etc).
**Describe alternatives you've considered**
Currently I manually load hdf files using `h5py` and implement PyTorch dataset interface. For small h5 files I load them into a pandas dataframe and use `from_pandas` function in the `datasets` package to load them, but for big datasets this is not feasible.
**Additional context**
HDF files are widespread throughout different domains and are one of the go to's for many researchers/scientists/engineers who work with numerical data. Given `datasets`' usecases have outgrown NLP use cases, it will make a lot of sense focusing on things like supporting HDF files.
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https://api.github.com/repos/huggingface/datasets/issues/3112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3112/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3112/comments | https://api.github.com/repos/huggingface/datasets/issues/3112/events | https://github.com/huggingface/datasets/issues/3112 | 1,030,613,083 | I_kwDODunzps49behb | 3,112 | OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB | {
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"I am very unsure on why you tagged me here. I am not a maintainer of the Datasets library and have no idea how to help you.",
"fixed",
"Ok got it, tensor full of NaNs, cf.\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:",
"Actually this is is a live bug, documented yet still live so reopening"
] | 1,634,667,701,000 | 1,634,669,549,000 | null | NONE | null | ## Describe the bug
Despite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :
> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
Note that I always run `batch_size=writer_batch_size` :
## Steps to reproduce the bug
```python
datasets.map(lambda example : {"column_name" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc="blablabla")
```
## Introspecting CUDA memory during bug
Placed within `function(arguments)` the following statement to introspect memory usage, merely a little over 1/4 of 2Gb
`print(torch.cuda.memory_summary(device=device, abbreviated=False))`
> |===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 0 | cudaMalloc retries: 0 |
|===========================================================================|
| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |
|---------------------------------------------------------------------------|
| Allocated memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| Active memory | 541418 KB | 545725 KB | 555695 KB | 14276 KB |
| from large pool | 540672 KB | 544431 KB | 544431 KB | 3759 KB |
| from small pool | 746 KB | 1714 KB | 11264 KB | 10517 KB |
|---------------------------------------------------------------------------|
| GPU reserved memory | 598016 KB | 598016 KB | 598016 KB | 0 B |
| from large pool | 595968 KB | 595968 KB | 595968 KB | 0 B |
| from small pool | 2048 KB | 2048 KB | 2048 KB | 0 B |
|---------------------------------------------------------------------------|
| Non-releasable memory | 36117 KB | 52292 KB | 274275 KB | 238158 KB |
| from large pool | 34816 KB | 51537 KB | 261713 KB | 226897 KB |
| from small pool | 1301 KB | 2045 KB | 12562 KB | 11261 KB |
|---------------------------------------------------------------------------|
| Allocations | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| Active allocs | 198 | 224 | 478 | 280 |
| from large pool | 74 | 75 | 75 | 1 |
| from small pool | 124 | 150 | 403 | 279 |
|---------------------------------------------------------------------------|
| GPU reserved segments | 21 | 21 | 21 | 0 |
| from large pool | 20 | 20 | 20 | 0 |
| from small pool | 1 | 1 | 1 | 0 |
|---------------------------------------------------------------------------|
| Non-releasable allocs | 18 | 23 | 166 | 148 |
| from large pool | 17 | 18 | 19 | 2 |
| from small pool | 1 | 6 | 147 | 146 |
|===========================================================================|
## Expected results
Efficiently process the datasets and write it down to disk.
## Actual results
--------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2390 else:
-> 2391 writer.write(example)
2392 else:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write(self, example, key, writer_batch_size)
367
--> 368 self.write_examples_on_file()
369
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
During handling of the above exception, another exception occurred:
OverflowError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_16268/2456940807.py in <module>
3 #tracker = OfflineEmissionsTracker(country_iso_code="FRA", project_name='xxx'+time_stamp,output_dir='./codecarbon')
4 #tracker.start()
----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,
6 clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,
7 max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #
~\xxx\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)
481 for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):
482 if "xxx_" in column:
--> 483 dataset = dataset.map(lambda example :
484 {"embeddings_"+str(column).replace("translated_",""):function(input_ids=example[column],
485 token_type_ids=example[column.replace("input_ids","token_type_ids")],
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
2034
2035 if num_proc is None or num_proc == 1:
-> 2036 return self._map_single(
2037 function=function,
2038 with_indices=with_indices,
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
501 self: "Dataset" = kwargs.pop("self")
502 # apply actual function
--> 503 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
504 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
505 for dataset in datasets:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~\anaconda3\envs\xxx\lib\site-packages\datasets\fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2425 if update_data:
2426 if writer is not None:
-> 2427 writer.finalize()
2428 if tmp_file is not None:
2429 tmp_file.close()
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in finalize(self, close_stream)
440 # Re-intializing to empty list for next batch
441 self.hkey_record = []
--> 442 self.write_examples_on_file()
443 if self.pa_writer is None:
444 if self._schema is not None:
~\anaconda3\envs\xxx\lib\site-packages\datasets\arrow_writer.py in write_examples_on_file(self)
315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:
316 if not isinstance(pa_array[0], pa.lib.FloatScalar):
--> 317 raise OverflowError(
318 "There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB".format(
319 type(pa_array)
OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: Windows-10-10.0.19042-SP0
- Python version: 3.8.11
- PyArrow version: 3.0.0
##Next steps
Testing on Linux.
@albertvillanova
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https://api.github.com/repos/huggingface/datasets/issues/3111 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3111/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3111/comments | https://api.github.com/repos/huggingface/datasets/issues/3111/events | https://github.com/huggingface/datasets/issues/3111 | 1,030,598,983 | I_kwDODunzps49bbFH | 3,111 | concatenate_datasets removes ClassLabel typing. | {
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"Something like this would fix it I think: https://github.com/huggingface/datasets/compare/master...Dref360:HF-3111/concatenate_types?expand=1"
] | 1,634,666,731,000 | 1,634,827,821,000 | 1,634,827,821,000 | CONTRIBUTOR | null | ## Describe the bug
When concatenating two datasets, we lose typing of ClassLabel columns.
I can work on this if this is a legitimate bug,
## Steps to reproduce the bug
```python
import datasets
from datasets import Dataset, ClassLabel, Value, concatenate_datasets
DS_LEN = 100
my_dataset = Dataset.from_dict(
{
"sentence": [f"{chr(i % 10)}" for i in range(DS_LEN)],
"label": [i % 2 for i in range(DS_LEN)]
}
)
my_predictions = Dataset.from_dict(
{
"pred": [(i + 1) % 2 for i in range(DS_LEN)]
}
)
my_dataset = my_dataset.cast(datasets.Features({"sentence": Value("string"), "label": ClassLabel(2, names=["POS", "NEG"])}))
print("Original")
print(my_dataset)
print(my_dataset.features)
concat_ds = concatenate_datasets([my_dataset, my_predictions], axis=1)
print("Concatenated")
print(concat_ds)
print(concat_ds.features)
```
## Expected results
The features of `concat_ds` should contain ClassLabel.
## Actual results
On master, I get:
```
{'sentence': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'pred': Value(dtype='int64', id=None)}
```
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3110 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3110/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3110/comments | https://api.github.com/repos/huggingface/datasets/issues/3110/events | https://github.com/huggingface/datasets/pull/3110 | 1,030,558,484 | PR_kwDODunzps4tZakS | 3,110 | Stream TAR-based dataset using iter_archive | {
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"I'm creating a new branch `stream-tar-audio` just for the audio datasets since they need https://github.com/huggingface/datasets/pull/3129 to be merged first",
"The CI fails are only related to missing sections or tags in the dataset cards - which is unrelated to this PR"
] | 1,634,663,784,000 | 1,636,134,529,000 | 1,636,134,528,000 | MEMBER | null | I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable.
It means that around 80 datasets become streamable :) | {
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https://api.github.com/repos/huggingface/datasets/issues/3108 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3108/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3108/comments | https://api.github.com/repos/huggingface/datasets/issues/3108/events | https://github.com/huggingface/datasets/pull/3108 | 1,030,405,618 | PR_kwDODunzps4tY8ID | 3,108 | Add Google BLEU (aka GLEU) metric | {
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} | [] | closed | false | null | [] | null | [] | 1,634,654,918,000 | 1,635,170,824,000 | 1,635,170,824,000 | CONTRIBUTOR | null | This PR adds the NLTK implementation of Google BLEU metric. This is also a part of an effort to resolve an unfortunate naming collision between GLEU for machine translation and GLEU for grammatical error correction.
I used [this page](https://huggingface.co/docs/datasets/add_metric.html) for reference. Please, point me to the right direction if I missed anything. | {
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} | [] | closed | false | null | [] | null | [] | 1,634,652,491,000 | 1,634,653,582,000 | 1,634,653,581,000 | MEMBER | null | Add paper BibTeX citation to README file. | {
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https://api.github.com/repos/huggingface/datasets/issues/3106 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3106/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3106/comments | https://api.github.com/repos/huggingface/datasets/issues/3106/events | https://github.com/huggingface/datasets/pull/3106 | 1,030,112,473 | PR_kwDODunzps4tYA6i | 3,106 | Fix URLs in blog_authorship_corpus dataset | {
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- the old URLs are no longer valid
- there are alternative host URLs
Fix #3091. | {
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] | open | false | null | [] | null | [] | 1,634,562,758,000 | 1,634,895,370,000 | null | CONTRIBUTOR | null | ## Describe the bug
If a cached dataset is removed from the library, I don't see how to delete it programmatically. I thought that using `force_redownload` would try to refresh the cache, then raise an exception, but it reuses the cache instead.
## Steps to reproduce the bug
_requires to already have `wit` in the cache_: see https://github.com/huggingface/datasets/pull/2981
```python
import datasets as ds
dataset = ds.load_dataset("wit", split="train", download_mode='force_redownload')
```
## Expected results
It should raise an exception, since the dataset does not exist anymore.
## Actual results
It uses the cached result
```
Using the latest cached version of the module from /home/slesage/.cache/huggingface/modules/datasets_modules/datasets/wit/107afbffd48e058b19101bddc47fbee25fa68eb6d50a733e262875f1285a5171 (last modified on Wed Sep 29 08:21:10 2021) since it couldn't be found locally at wit, or remotely on the Hugging Face Hub.
```
## Environment info
- `datasets` version: 1.13.4.dev0
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
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"Zenodo has fixed on their side the 1.13.3 release: https://zenodo.org/record/5589150"
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TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed | {
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Currently, `datasets` project description appearing in PyPI shows the release instructions addressed to core maintainers: https://pypi.org/project/datasets/1.13.3/
Fix #3102. | {
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https://api.github.com/repos/huggingface/datasets/issues/3101 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3101/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3101/comments | https://api.github.com/repos/huggingface/datasets/issues/3101/events | https://github.com/huggingface/datasets/pull/3101 | 1,028,966,968 | PR_kwDODunzps4tUelE | 3,101 | Update SUPERB to use Audio features | {
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"Thank you! Sorry I forgot this one @albertvillanova"
] | 1,634,555,118,000 | 1,634,560,434,000 | 1,634,558,806,000 | CONTRIBUTOR | null | This is the same dataset refresh as the other Audio ones: https://github.com/huggingface/datasets/pull/3081
cc @patrickvonplaten | {
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To fix #3097, there are 2 alternatives:
- Either pinning `fsspec` to versions newer or equal to `2021.06.0`
- Or replacing `fsspec.FSTimeoutError` wth its parent `asyncio.TimeoutError`, which exists from Python 3.8.0 (Sep 2018).
This PR implements the second approach.
Fix #3097. | {
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"Hi @JTWang2000, thanks for reporting.\r\n\r\nHowever, I cannot reproduce your reported bug:\r\n```python\r\n>>> from datasets import load_dataset\r\n\r\n>>> dataset = load_dataset(\"sst\", \"default\")\r\n>>> dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 8544\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 1101\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'tokens', 'tree'],\r\n num_rows: 2210\r\n })\r\n})\r\n```\r\n\r\nMaybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n```\r\npip install -U huggingface_hub\r\n```",
"Im facing the same issue. I did run the upgrade command but that doesnt seem to resolve the issue",
"Hi @aneeshjain, could you please specify which `huggingface_hub` version you are using?\r\n\r\nBesides that, please run `datasets-cli env` and copy-and-paste its output below.",
"The problem seems to be with the latest version of `datasets`. After running `pip install -U datasets huggingface_hub`, I get the following: \r\n\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/__init__.py\", line 37, in <module>\r\n from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/builder.py\", line 44, in <module>\r\n from .data_files import DataFilesDict, _sanitize_patterns\r\n File \"/opt/conda/lib/python3.6/site-packages/datasets/data_files.py\", line 122, in <module>\r\n allowed_extensions: Optional[list] = None,\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n````\r\nNote that pip reports the latest `datasets` version as \r\n```bash\r\n pip show datasets\r\nName: datasets\r\nVersion: 1.14.0\r\n```\r\nHowever, if I downgrade datasets with `pip install datasets==1.11.0`, things now work\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\ndvers=1.11.0\r\n````",
"> Hi @JTWang2000, thanks for reporting.\r\n> \r\n> However, I cannot reproduce your reported bug:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> \r\n> >>> dataset = load_dataset(\"sst\", \"default\")\r\n> >>> dataset\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 8544\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 1101\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'tokens', 'tree'],\r\n> num_rows: 2210\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Maybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n> \r\n> ```\r\n> pip install -U huggingface_hub\r\n> ```\r\n\r\nMy problem solved after updating huggingface hub command. Thanks a lot and sorry for the late reply. ",
"@tjruwase, please note that versions of `datsets` and `huggingface_hub` must be compatible one with each other:\r\n- In `datasets` version `1.11.0`, the requirement on `huggingface_hub` was: `huggingface_hub<0.1.0`\r\n https://github.com/huggingface/datasets/blob/2cc00f372a96133e701275eec4d6b26d15257289/setup.py#L90\r\n - Therefore, your installed `huggingface_hub` version `0.0.8` was compatible\r\n- In `datasets` version `1.12.0`, the requirement on `huggingface_hub` was: `huggingface_hub>=0.0.14,<0.1.0`\r\n https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/setup.py#L104\r\n - Therefore, your installed `huggingface_hub` version `0.0.8` was no longer compatible \r\n- Currently, in `datasets` version `1.15.1`, the requirement on `huggingface_hub` is: `huggingface_hub>=0.1.0,<1.0.0`\r\n https://github.com/huggingface/datasets/blob/018100679d21cf27136f0eccb1c50e3a9c968ce2/setup.py#L102\r\n\r\n@JTWang2000, thanks for your answer. I close this issue then."
] | 1,634,480,267,000 | 1,636,476,149,000 | 1,636,476,148,000 | NONE | null | ## Describe the bug
When using `pip install datasets`
or use `conda install -c huggingface -c conda-forge datasets`
cannot install datasets
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("sst", "default")
```
## Actual results
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-fbe7981e6e21> in <module>
1 import torch
2 import transformers
----> 3 from datasets import load_dataset
4
5 dataset = load_dataset("sst", "default")
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/__init__.py in <module>
35 from .arrow_reader import ArrowReader, ReadInstruction
36 from .arrow_writer import ArrowWriter
---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
38 from .combine import interleave_datasets
39 from .dataset_dict import DatasetDict, IterableDatasetDict
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/builder.py in <module>
42 )
43 from .arrow_writer import ArrowWriter, BeamWriter
---> 44 from .data_files import DataFilesDict, _sanitize_patterns
45 from .dataset_dict import DatasetDict, IterableDatasetDict
46 from .fingerprint import Hasher
~/miniforge3/envs/actor/lib/python3.8/site-packages/datasets/data_files.py in <module>
118
119 def _exec_patterns_in_dataset_repository(
--> 120 dataset_info: huggingface_hub.hf_api.DatasetInfo,
121 patterns: List[str],
122 allowed_extensions: Optional[list] = None,
AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.3
- Platform: macOS-11.3.1-arm64-arm-64bit
- Python version: 3.8.10
- PyArrow version: 5.0.0
| {
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https://api.github.com/repos/huggingface/datasets/issues/3098 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3098/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3098/comments | https://api.github.com/repos/huggingface/datasets/issues/3098/events | https://github.com/huggingface/datasets/pull/3098 | 1,028,210,790 | PR_kwDODunzps4tSRSZ | 3,098 | Push to hub capabilities for `Dataset` and `DatasetDict` | {
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"Thank you for your reviews! I should have addressed all of your comments, and I added a test to ensure that `private` datasets work correctly too. I have merged the changes in `huggingface_hub`, so the `main` branch can be installed now; and I will release v0.1.0 soon.\r\n\r\nAs blockers for this PR:\r\n- It's still waiting for #3027 to be addressed as the folder name will dictate the split name\r\n- The `self.split` name is set to `None` when the dataset dict is instantiated as follows:\r\n```py\r\nds = Dataset.from_dict({\"x\": [1, 2, 3], \"y\": [4, 5, 6]})\r\nlocal_ds = DatasetDict({\"random\": ds})\r\n\r\nlocal_ds['random'].split # returns None\r\n```\r\nIn order to remove the `split=key` I would need to know of a different way to test here as it relies on the above as a surefire way of constructing a `DatasetDict`.\r\n- Finally, the `threading` parameter is flaky on moon-staging which results in many errors server side. I propose to leave it as an argument instead of having it having it set to `True` so that users may toggle it according to their wish. ",
"Currently it looks like it only saves the last split.\r\nIndeed when writing the data of one split, it deletes all the other files from the other splits\r\n```python\r\n>>> dataset.push_to_hub(\"lhoestq/squad_titles\", shard_size=50<<10) \r\nPushing split train to the Hub.\r\nPushing dataset shards to the dataset hub: 100%|β| 31/31 [00:22<00:00, 1.38\r\nPushing split validation to the Hub.\r\nThe repository already exists: the `private` keyword argument will be ignored.\r\nDeleting unused files from dataset repository: 100%|β| 31/31 [00:14<00:00, \r\nPushing dataset shards to the dataset hub: 100%|β| 4/4 [00:03<00:00, 1.18it\r\n```\r\nNote the \"Deleting\" part.",
"I think this PR should fix #3035, so feel free to link it. ",
"Thank you for your comments! I have rebased on `master` to have PR #3221. I've updated all tests to reflect the `-` instead of the `_` in the filenames.\r\n\r\n@lhoestq, I have fixed the issue with splits and added a corresponding test.\r\n\r\n@mariosasko I have not updated the `load_dataset` method to work differently, so I don't expect #3035 to be resolved with `push_to_hub`.\r\n\r\nOnly remaining issues before merging:\r\n- Take a good look at the `threading` and if that's something we want to keep.\r\n- As mentioned above:\r\n>The self.split name is set to None when the dataset dict is instantiated as follows:\r\n> ```\r\n> ds = Dataset.from_dict({\"x\": [1, 2, 3], \"y\": [4, 5, 6]})\r\n> local_ds = DatasetDict({\"random\": ds})\r\n> \r\n> local_ds['random'].split # returns None\r\n> ```\r\nI need to understand how to build a `DatasetDict` from some `Dataset` objects to be able to leverage the `split` parameter in `DatasetDict.push_to_hub`",
"Cool thanks ! And indeed this won't solve https://github.com/huggingface/datasets/issues/3035 yet\r\n\r\n> I need to understand how to build a DatasetDict from some Dataset objects to be able to leverage the split parameter in DatasetDict.push_to_hub\r\n\r\nYou can use the key in the DatasetDict instead of the `split` attribute",
"What do you think about bumping the minimum version of pyarrow to 3.0.0 ? This is the minimum required version to write parquet files, which is needed for push_to_hub. That's why our pyarrow 1 CI is failing.\r\n\r\nI think it's fine since it's been available for a long time (january 2021) and it's also the version that is installed on Google Colab.",
"Pushing pyarrow to 3.0.0 is fine for me. I donβt think we need to keep a lot of backward support for pyarrow.",
"Hi.\r\nI published in the forum about my experience with `DatasetDict.push_to_hub()`: here is my [post.](https://discuss.huggingface.co/t/save-datasetdict-to-huggingface-hub/12075/4)\r\nOn my side, there is a problem as my train and validation `Datasets` are concatenated when I do a `load_dataset()` from the `DatasetDict` I pushed to the HF datasets hub.",
"Hi ! Let me respond here as well in case other people have the same issues and come here:\r\n\r\n`push_to_hub` was introduced in `datasets` 1.16, and to be able to properly load a dataset with separated splits you need to have `datasets>=1.16.0` as well. \r\n\r\nOld version of `datasets` used to concatenate everything in the `train` split."
] | 1,634,443,964,000 | 1,638,979,490,000 | 1,637,753,136,000 | MEMBER | null | This PR implements a `push_to_hub` method on `Dataset` and `DatasetDict`. This does not currently work in `IterableDatasetDict` nor `IterableDataset` as those are simple dicts and I would like your opinion on how you would like to implement this before going ahead and doing it.
This implementation needs to be used with the following `huggingface_hub` branch in order to work correctly: https://github.com/huggingface/huggingface_hub/pull/415
### Implementation
The `push_to_hub` API is entirely based on HTTP requests rather than a git-based workflow:
- This allows pushing changes without firstly cloning the repository, which reduces the time in half for the `push_to_hub` method.
- Collaboration, as well as the system of branches/merges/rebases is IMO less straightforward than for models and spaces. In the situation where such collaboration is needed, I would *heavily* advocate for the `Repository` helper of the `huggingface_hub` to be used instead of the `push_to_hub` method which will always be, by design, limiting in that regard (even if based on a git-workflow instead of HTTP requests)
In order to overcome the limit of 5GB files set by the HTTP requests, dataset sharding is used.
### Testing
The test suite implemented here makes use of the moon-staging instead of the production setup. As several repositories are created and deleted, it is better to use the staging.
It does not require setting an environment variable or any kind of special attention but introduces a new decorator `with_staging_testing` which patches global variables to use the staging endpoint instead of the production endpoint.
### Examples
The tests cover a lot of examples and behavior. | {
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https://api.github.com/repos/huggingface/datasets/issues/3097 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3097/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3097/comments | https://api.github.com/repos/huggingface/datasets/issues/3097/events | https://github.com/huggingface/datasets/issues/3097 | 1,027,750,811 | I_kwDODunzps49Qjub | 3,097 | `ModuleNotFoundError: No module named 'fsspec.exceptions'` | {
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"Thanks for reporting, @VictorSanh.\r\n\r\nI'm fixing it."
] | 1,634,326,478,000 | 1,634,543,514,000 | 1,634,543,514,000 | MEMBER | null | ## Describe the bug
I keep runnig into a fsspec ModuleNotFound error
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_infos
2021-10-15 15:25:37.863206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-10-15 15:25:37.863252: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 56, in <module>
from .utils.streaming_download_manager import StreamingDownloadManager
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/streaming_download_manager.py", line 11, in <module>
from fsspec.exceptions import FSTimeoutError
ModuleNotFoundError: No module named 'fsspec.exceptions'
```
Yet, I do have `fsspec`:
```bash
hf@victor-scale:~/dev/promptsource$ pip show fsspec
Name: fsspec
Version: 2021.5.0
Summary: File-system specification
Home-page: http://github.com/intake/filesystem_spec
Author: None
Author-email: None
License: BSD
Location: /home/hf/dev/promptsource/.venv/lib/python3.7/site-packages
Requires:
Required-by: datasets
```
With the same version of fsspec and `datasets==1.9.0`, I don't see this problem....
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
I can't even run `datasets-cli env` actually.., but here's my env:
- `datasets` version: 1.13.3
- Platform: Ubuntu 18.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3096 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3096/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3096/comments | https://api.github.com/repos/huggingface/datasets/issues/3096/events | https://github.com/huggingface/datasets/pull/3096 | 1,027,535,685 | PR_kwDODunzps4tQblQ | 3,096 | Fix Audio feature mp3 resampling | {
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This PR fixes Audio feature mp3 resampling.
Fix #3095. | {
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https://api.github.com/repos/huggingface/datasets/issues/3095 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3095/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3095/comments | https://api.github.com/repos/huggingface/datasets/issues/3095/events | https://github.com/huggingface/datasets/issues/3095 | 1,027,453,146 | I_kwDODunzps49PbDa | 3,095 | `cast_column` makes audio decoding fail | {
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"cc @anton-l @albertvillanova ",
"Thanks for reporting, @patrickvonplaten.\r\n\r\nI think the issue is related to mp3 resampling, not to `cast_column`.\r\n\r\nYou can check that `cast_column` works OK with non-mp3 audio files:\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\nds = load_dataset(\"arabic_speech_corpus\", split=\"train\")\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\nprint(ds[0][\"audio\"])\r\n```\r\n\r\nI'm fixing it."
] | 1,634,305,018,000 | 1,634,312,310,000 | 1,634,312,310,000 | MEMBER | null | ## Describe the bug
After changing the sampling rate automatic decoding fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import datasets
ds = load_dataset("common_voice", "ab", split="train")
ds = ds.cast_column("audio", datasets.features.Audio(sampling_rate=16_000))
print(ds[0]["audio"]) # <- this fails currently
```
yields:
```
TypeError: forward() takes 2 positional arguments but 4 were given
```
## Expected results
no failure
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 1.13.2 (master)
- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3094 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3094/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3094/comments | https://api.github.com/repos/huggingface/datasets/issues/3094/events | https://github.com/huggingface/datasets/issues/3094 | 1,027,328,633 | I_kwDODunzps49O8p5 | 3,094 | Support loading a dataset from SQLite files | {
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"Hi, \r\n\r\neven Pandas, which is less strict compared to PyArrow when it comes to reading JSON, doesn't support different orderings:\r\n```python\r\nimport io\r\nimport pandas as pd\r\n\r\ns = \"\"\"\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n\"\"\"\r\n\r\nbuffer = io.StringIO(s)\r\ndf = pd.read_json(buffer, lines=True)\r\n\r\nprint(df.shape[0]) # 0\r\n```\r\n\r\nSo we can't even fall back to Pandas in such cases.\r\n\r\nIt seems the only option is a script that recursively re-orders fields to enforce deterministic order:\r\n```python\r\nwith open(\"train.json\", \"r\") as fin:\r\n with open(\"train_reordered.json\", \"w\") as fout:\r\n for line in fin:\r\n obj_jsonl = json.loads(line.strip())\r\n fout.write(json.dumps(obj_jsonl, sort_keys=True) + \"\\n\")\r\n```"
] | 1,634,290,405,000 | 1,636,550,434,000 | null | NONE | null | ## Describe the bug
Loading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below.
If the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine.
## Steps to reproduce the bug
Create two json files:
train.json
```
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
```
test.json
```
{"a": {"b": 1, "c": 2}}
{"a": {"b": 3, "c": 4}}
```
```python
from datasets import load_dataset
# Loading the files individually works (even though the keys in train.json don't have the same order)
load_dataset('json', data_files={"test": "test.json"})
load_dataset('json', data_files={"train": "train.json"})
# Loading both splits fails
load_dataset('json', data_files={"train": "train.json", "test": "test.json"})
```
## Expected results
Loading both splits should not give an error whether the nested dicts are have the same order or not.
## Actual results
```
>>> load_dataset('json', data_files={"train": "train.json", "test": "test.json"})
Using custom data configuration default-f1bc76fd07398c4c
Downloading and preparing dataset json/default to /home/dthulke/.cache/huggingface/datasets/json/default-f1bc76fd07398c4c/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 8839.42it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 477.82it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1596, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 592, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 329, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 277, in pyarrow.lib.ChunkedArray.cast
File "/home/dthulke/venvs/venv_torch_transformers/lib/python3.6/site-packages/pyarrow/compute.py", line 297, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 527, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 337, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.13.2
- Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyArrow version: 5.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3092 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3092/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3092/comments | https://api.github.com/repos/huggingface/datasets/issues/3092/events | https://github.com/huggingface/datasets/pull/3092 | 1,027,260,383 | PR_kwDODunzps4tPj6e | 3,092 | Fix JNLBA dataset | {
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"Fix #3089.",
"@albertvillanova all tests are passing now. Either you or @lhoestq can review it!"
] | 1,634,290,274,000 | 1,634,891,037,000 | 1,634,891,037,000 | CONTRIBUTOR | null | As mentioned in #3089, I've added more tags and also updated the link for dataset which was earlier using a Google Drive link.
I'm having problem with generating dummy data as `datasets-cli dummy_data ./datasets/jnlpba --auto_generate --match_text_files "*.iob2"` is giving `datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
` error. I'll try to add dummy data manually. | {
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"Hi @fdtomasi, thanks for reporting.\r\n\r\nYou are right: the original host data URL does no longer exist.\r\n\r\nI've contacted the authors of the dataset to ask them if they host this dataset in another URL.",
"Hi, @fdtomasi, the URL is fixed.\r\n\r\nThe fix is already in our master branch and it will be accessible in our next release.\r\n\r\nIn the meantime, you can include the fix if you install the `datasets` library from the master branch:\r\n```\r\npip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```",
"Awesome thank you so much for the quick fix!"
] | 1,634,289,640,000 | 1,634,648,770,000 | 1,634,647,839,000 | NONE | null | ## Describe the bug
The dataset `blog_authorship_corpus` is broken.
By bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.
I suspect it is because the data download url is broken (http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip).
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("blog_authorship_corpus", split="train", download_mode='force_redownload')
```
## Expected results
No error.
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
/tmp/ipykernel_5237/1729238701.py in <module>
2 ds = load_dataset(
3 "blog_authorship_corpus", split="train",
----> 4 download_mode='force_redownload'
5 )
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)
1115 ignore_verifications=ignore_verifications,
1116 try_from_hf_gcs=try_from_hf_gcs,
-> 1117 use_auth_token=use_auth_token,
1118 )
1119
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
635 if not downloaded_from_gcs:
636 self._download_and_prepare(
--> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
638 )
639 # Sync info
/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
707 if verify_infos:
708 verify_checksums(
--> 709 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
710 )
711
/opt/conda/lib/python3.7/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://www.cs.biu.ac.il/~koppel/blogs/blogs.zip']
```
## Environment info
- `datasets` version: 1.13.2
- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11
- Python version: 3.7.10
- PyArrow version: 5.0.0 | {
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