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README.md CHANGED
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- title: People Mate
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- emoji: πŸƒ
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- colorFrom: red
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- colorTo: gray
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- sdk: gradio
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- sdk_version: 3.44.4
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- app_file: app.py
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- pinned: false
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - feature-extraction
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+ - sentence-similarity
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+ language: en
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+ license: apache-2.0
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+ datasets:
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+ - s2orc
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+ - flax-sentence-embeddings/stackexchange_xml
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+ - ms_marco
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+ - gooaq
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+ - yahoo_answers_topics
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+ - code_search_net
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+ - search_qa
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+ - eli5
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+ - snli
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+ - multi_nli
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+ - wikihow
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+ - natural_questions
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+ - trivia_qa
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+ - embedding-data/sentence-compression
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+ - embedding-data/flickr30k-captions
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+ - embedding-data/altlex
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+ - embedding-data/simple-wiki
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+ - embedding-data/QQP
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+ - embedding-data/SPECTER
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+ - embedding-data/PAQ_pairs
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+ - embedding-data/WikiAnswers
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+
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  ---
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+
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+ # all-MiniLM-L6-v2
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ ## Usage (Sentence-Transformers)
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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+ model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+ ## Usage (HuggingFace Transformers)
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+ Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+ import torch
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+ import torch.nn.functional as F
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+
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+ #Mean Pooling - Take attention mask into account for correct averaging
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+ def mean_pooling(model_output, attention_mask):
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+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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+
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+
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+ # Sentences we want sentence embeddings for
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+ sentences = ['This is an example sentence', 'Each sentence is converted']
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+
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+ # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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+ model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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+
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+ # Tokenize sentences
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+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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+
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+ # Compute token embeddings
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+ with torch.no_grad():
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+ model_output = model(**encoded_input)
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+
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+ # Perform pooling
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+ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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+
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+ # Normalize embeddings
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+ sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
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+
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+ print("Sentence embeddings:")
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+ print(sentence_embeddings)
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+ ```
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+
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+ ## Evaluation Results
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+
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
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+
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+ ------
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+
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+ ## Background
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+
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+ The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
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+ contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
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+ 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
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+
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+ We developped this model during the
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+ [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
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+ organized by Hugging Face. We developped this model as part of the project:
109
+ [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
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+
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+ ## Intended uses
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+
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+ Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
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+ the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
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+
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+ By default, input text longer than 256 word pieces is truncated.
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+
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+
119
+ ## Training procedure
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+
121
+ ### Pre-training
122
+
123
+ We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
124
+
125
+ ### Fine-tuning
126
+
127
+ We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
128
+ We then apply the cross entropy loss by comparing with true pairs.
129
+
130
+ #### Hyper parameters
131
+
132
+ We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
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+ We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
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+ a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
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+
136
+ #### Training data
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+
138
+ We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
139
+ We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
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+
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+
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+ | Dataset | Paper | Number of training tuples |
143
+ |--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
144
+ | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
145
+ | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
146
+ | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
147
+ | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
148
+ | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
149
+ | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
150
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
151
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
152
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
153
+ | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
154
+ | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
155
+ | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
156
+ | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
157
+ | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
158
+ | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
159
+ | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
160
+ | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
161
+ | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
162
+ | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
163
+ | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
164
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
165
+ | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
166
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
167
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
168
+ | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
169
+ | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
170
+ | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
171
+ | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
172
+ | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
173
+ | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
174
+ | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
175
+ | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
176
+ | **Total** | | **1,170,060,424** |
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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last.py ADDED
@@ -0,0 +1,165 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import time
2
+
3
+ import chromadb
4
+ from chromadb.utils import embedding_functions
5
+ from test.new import connect_to_llama
6
+ # from transformers import pipeline
7
+ import gradio as gr
8
+ import PyPDF2
9
+ import os
10
+ from chunkipy.text_chunker import split_by_sentences
11
+ import langid
12
+ from translate import Translator
13
+
14
+ chroma_client = chromadb.PersistentClient()
15
+ from test.llama import llama_local
16
+ working_dir = os.getcwd()
17
+ # checkpoint = f"{working_dir}/LaMini-T5-738M"
18
+ # model = pipeline('text2text-generation', model=checkpoint)
19
+ # input_prompt = """Answer the following question related reasoning answers from the following contexts that is given ..Don't generate answer from your data generate only from the provided contexts
20
+ # ..If the contexts doesn't provide an answer or isn't related to the question, respond with "there is no answer for the provided question"
21
+ # Question:"{}",
22
+ # Contexts:"{}"
23
+ # Answer:
24
+ # """
25
+
26
+ def detect_and_translate_query(query, context, dest_language='en'):
27
+ input_language, _ = langid.classify(query)
28
+ if isinstance(context, list):
29
+ context = " ".join(context)
30
+ translator = Translator(to_lang=dest_language, from_lang=input_language)
31
+ translated_query = translator.translate(query)
32
+ translated_context = translator.translate(context)
33
+ return translated_query, translated_context, input_language
34
+
35
+ def translate_response(response, source_language, dest_language):
36
+ translator = Translator(to_lang=source_language, from_lang=dest_language)
37
+ translated_response = translator.translate(response)
38
+ print("translate_response "+str(translate_response))
39
+ return translated_response
40
+ def create_multiple_db(path,collection,working_dir):
41
+ filelist = os.listdir(path)
42
+ print(filelist)
43
+ data_pdfs = []
44
+ metadata_buff=[]
45
+ for file_n in filelist:
46
+ with open(file_n, 'rb') as file:
47
+ pdf_reader = PyPDF2.PdfReader(file)
48
+ meta_data=dict(pdf_reader.metadata)
49
+ print("De elmeta data before: ",meta_data)
50
+ meta_data.update({"/Title":file_n})
51
+ print("De elmeta data after: ", meta_data)
52
+ metadata_buff.append(meta_data)
53
+ data = ""
54
+ for page_num in range(len(pdf_reader.pages)):
55
+ data += pdf_reader.pages[page_num].extract_text()
56
+ chunk = split_by_sentences(data)
57
+ for i, chunks in enumerate(chunk):
58
+ print(f"chunks{i}:", chunks)
59
+ data_pdfs.append(chunk)
60
+ file.close()
61
+ os.chdir(working_dir)
62
+ print(metadata_buff,"\n",len(metadata_buff))
63
+ sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
64
+ i = 0
65
+ md_i=0
66
+ for data in data_pdfs:
67
+ print(data)
68
+ collection.add(
69
+ documents=data,
70
+ embeddings=sentence_transformer_ef(data),
71
+ ids=['id' + str(x + i) for x in range(len(data))],
72
+ metadatas=[metadata_buff[md_i]for i in range(len(data))]
73
+ )
74
+ md_i+=1
75
+ i += len(data)
76
+ return "done"
77
+
78
+ def architecture_with_chroma(data):
79
+ try:
80
+ data_dict = eval(data)
81
+ except:
82
+ return "please enter a valid json (dict) to process"
83
+ id = data_dict.get('id')
84
+ if id is None:
85
+ return "please enter an id to process on the prompt"
86
+ id = "mate" + str(id)
87
+ query = data_dict.get('query')
88
+ if query is None or query == "":
89
+ return "please enter a query to process"
90
+
91
+ collection = chroma_client.get_or_create_collection(name=id)
92
+ results = collection.query(
93
+ query_texts=[query],
94
+ n_results=5
95
+ )
96
+ context = results.get('documents')[0]
97
+ results_metadata = list(results.get("metadatas")[0])
98
+ results_documents = list(results.get("documents")[0])
99
+ for i in range(5):
100
+ results_documents[i] = f"In {results_metadata[i].get('/Title')}:" + results_documents[i]
101
+ for data in results_documents:
102
+ print(data)
103
+ print(context)
104
+ # generated_text = model(input_prompt.format(query+"? answer reasoning answers from the provided contexts only that is related and contains this information ", context), max_length=1024, do_sample=False)[0]['generated_text']
105
+ # print(input_prompt)
106
+ chroma_client.stop()
107
+ translated_query, translated_context, input_language = detect_and_translate_query(query, context)
108
+ print('translated_query '+str(translated_query))
109
+ print('translated_context '+str(translated_context))
110
+ results=connect_to_llama(query,results_documents)
111
+ # results=llama_local(query,results_documents)
112
+ translated_response = translate_response(results, input_language, dest_language='en')
113
+ return translated_response
114
+ # return results
115
+ # return generated_text
116
+ def create(data):
117
+ print(data)
118
+ print(type(data))
119
+ try:
120
+ dict=eval(data)
121
+ except:
122
+ return "please enter a valid json (dict) to process"
123
+ id=dict.get('id')
124
+ if id==None :
125
+ return "please enter an id to process on the prompt"
126
+ id="mate"+str(id)
127
+ if(not os.path.exists(id)):
128
+ return "sorry ,there is no directory for this client"
129
+ else:
130
+ chroma_client.delete_collection(name=id)
131
+ collection = chroma_client.get_or_create_collection(name=id)
132
+ print(os.chdir(id))
133
+ return create_multiple_db(os.getcwd(),collection,working_dir)+" making data for client"
134
+
135
+ def update(data):
136
+ print(data)
137
+ print(type(data))
138
+ try:
139
+ dict=eval(data)
140
+ except:
141
+ return "please enter a valid json (dict) to process"
142
+ id=dict.get('id')
143
+ if id==None :
144
+ return "please enter an id to process on the prompt"
145
+ id="mate"+str(dict.get('id'))
146
+ if(not os.path.exists(id)):
147
+ return "sorry ,there is no directory for this client"
148
+ else:
149
+ chroma_client.delete_collection(name=id)
150
+ collection=chroma_client.create_collection(name=id)
151
+ print(os.chdir(id))
152
+ return create_multiple_db(os.getcwd(),collection,working_dir)+"updating client embeddings"
153
+
154
+ iface = gr.Blocks()
155
+ with iface:
156
+ name = gr.Textbox(label="Name")
157
+ output = gr.Textbox(label="Output Box")
158
+ process_btn = gr.Button("process")
159
+ process_btn.click(fn=architecture_with_chroma, inputs=name, outputs=output, api_name="process")
160
+ create_btn = gr.Button("create")
161
+ create_btn.click(fn=create, inputs=name, outputs=output, api_name="create")
162
+ update_btn = gr.Button("update")
163
+ update_btn.click(fn=update, inputs=name, outputs=output, api_name="update")
164
+
165
+ iface.launch()
modules.json ADDED
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+ "type": "sentence_transformers.models.Pooling"
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+ },
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+ {
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+ "idx": 2,
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+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
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+ }
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train_script.py ADDED
@@ -0,0 +1,344 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Train script for a single file
3
+
4
+ Need to set the TPU address first:
5
+ export XRT_TPU_CONFIG="localservice;0;localhost:51011"
6
+ """
7
+
8
+ import torch.multiprocessing as mp
9
+ import threading
10
+ import time
11
+ import random
12
+ import sys
13
+ import argparse
14
+ import gzip
15
+ import json
16
+ import logging
17
+ import tqdm
18
+ import torch
19
+ from torch import nn
20
+ from torch.utils.data import DataLoader
21
+ import torch
22
+ import torch_xla
23
+ import torch_xla.core
24
+ import torch_xla.core.functions
25
+ import torch_xla.core.xla_model as xm
26
+ import torch_xla.distributed.xla_multiprocessing as xmp
27
+ import torch_xla.distributed.parallel_loader as pl
28
+ import os
29
+ from shutil import copyfile
30
+
31
+
32
+ from transformers import (
33
+ AdamW,
34
+ AutoModel,
35
+ AutoTokenizer,
36
+ get_linear_schedule_with_warmup,
37
+ set_seed,
38
+ )
39
+
40
+ class AutoModelForSentenceEmbedding(nn.Module):
41
+ def __init__(self, model_name, tokenizer, normalize=True):
42
+ super(AutoModelForSentenceEmbedding, self).__init__()
43
+
44
+ self.model = AutoModel.from_pretrained(model_name)
45
+ self.normalize = normalize
46
+ self.tokenizer = tokenizer
47
+
48
+ def forward(self, **kwargs):
49
+ model_output = self.model(**kwargs)
50
+ embeddings = self.mean_pooling(model_output, kwargs['attention_mask'])
51
+ if self.normalize:
52
+ embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
53
+
54
+ return embeddings
55
+
56
+ def mean_pooling(self, model_output, attention_mask):
57
+ token_embeddings = model_output[0] # First element of model_output contains all token embeddings
58
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
59
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
60
+
61
+ def save_pretrained(self, output_path):
62
+ if xm.is_master_ordinal():
63
+ self.tokenizer.save_pretrained(output_path)
64
+ self.model.config.save_pretrained(output_path)
65
+
66
+ xm.save(self.model.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
67
+
68
+
69
+
70
+
71
+ def train_function(index, args, queue):
72
+ tokenizer = AutoTokenizer.from_pretrained(args.model)
73
+ model = AutoModelForSentenceEmbedding(args.model, tokenizer)
74
+
75
+
76
+ ### Train Loop
77
+ device = xm.xla_device()
78
+ model = model.to(device)
79
+
80
+ # Instantiate optimizer
81
+ optimizer = AdamW(params=model.parameters(), lr=2e-5, correct_bias=True)
82
+
83
+ lr_scheduler = get_linear_schedule_with_warmup(
84
+ optimizer=optimizer,
85
+ num_warmup_steps=500,
86
+ num_training_steps=args.steps,
87
+ )
88
+
89
+ # Now we train the model
90
+ cross_entropy_loss = nn.CrossEntropyLoss()
91
+ max_grad_norm = 1
92
+
93
+ model.train()
94
+
95
+ for global_step in tqdm.trange(args.steps, disable=not xm.is_master_ordinal()):
96
+ #### Get the batch data
97
+ batch = queue.get()
98
+ #print(index, "batch {}x{}".format(len(batch), ",".join([str(len(b)) for b in batch])))
99
+
100
+
101
+ if len(batch[0]) == 2: #(anchor, positive)
102
+ text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
103
+ text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
104
+
105
+ ### Compute embeddings
106
+ embeddings_a = model(**text1.to(device))
107
+ embeddings_b = model(**text2.to(device))
108
+
109
+ ### Gather all embedings
110
+ embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
111
+ embeddings_b = torch_xla.core.functions.all_gather(embeddings_b)
112
+
113
+ ### Compute similarity scores 512 x 512
114
+ scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
115
+
116
+ ### Compute cross-entropy loss
117
+ labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
118
+
119
+ ## Symmetric loss as in CLIP
120
+ loss = (cross_entropy_loss(scores, labels) + cross_entropy_loss(scores.transpose(0, 1), labels)) / 2
121
+
122
+ else: #(anchor, positive, negative)
123
+ text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
124
+ text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
125
+ text3 = tokenizer([b[2] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
126
+
127
+ embeddings_a = model(**text1.to(device))
128
+ embeddings_b1 = model(**text2.to(device))
129
+ embeddings_b2 = model(**text3.to(device))
130
+
131
+ embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
132
+ embeddings_b1 = torch_xla.core.functions.all_gather(embeddings_b1)
133
+ embeddings_b2 = torch_xla.core.functions.all_gather(embeddings_b2)
134
+
135
+ embeddings_b = torch.cat([embeddings_b1, embeddings_b2])
136
+
137
+ ### Compute similarity scores 512 x 1024
138
+ scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
139
+
140
+ ### Compute cross-entropy loss
141
+ labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
142
+
143
+ ## One-way loss
144
+ loss = cross_entropy_loss(scores, labels)
145
+
146
+
147
+ # Backward pass
148
+ optimizer.zero_grad()
149
+ loss.backward()
150
+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
151
+
152
+ xm.optimizer_step(optimizer, barrier=True)
153
+ lr_scheduler.step()
154
+
155
+
156
+ #Save model
157
+ if (global_step+1) % args.save_steps == 0:
158
+ output_path = os.path.join(args.output, str(global_step+1))
159
+ xm.master_print("save model: "+output_path)
160
+ model.save_pretrained(output_path)
161
+
162
+
163
+ output_path = os.path.join(args.output, "final")
164
+ xm.master_print("save model final: "+ output_path)
165
+ model.save_pretrained(output_path)
166
+
167
+
168
+ def produce_data(args, queue, filepaths, dataset_indices):
169
+ global_batch_size = args.batch_size*args.nprocs #Global batch size
170
+ size_per_dataset = int(global_batch_size / args.datasets_per_batch) #How many datasets per batch
171
+ num_same_dataset = int(size_per_dataset / args.batch_size)
172
+ print("producer", "global_batch_size", global_batch_size)
173
+ print("producer", "size_per_dataset", size_per_dataset)
174
+ print("producer", "num_same_dataset", num_same_dataset)
175
+
176
+ datasets = []
177
+ for filepath in filepaths:
178
+ if "reddit_" in filepath: #Special dataset class for Reddit files
179
+ data_obj = RedditDataset(filepath)
180
+ else:
181
+ data_obj = Dataset(filepath)
182
+ datasets.append(iter(data_obj))
183
+
184
+ # Store if dataset is in a 2 col or 3 col format
185
+ num_cols = {idx: len(next(dataset)) for idx, dataset in enumerate(datasets)}
186
+
187
+ while True:
188
+ texts_in_batch = set()
189
+ batch_format = None #2 vs 3 col format for this batch
190
+
191
+ #Add data from several sub datasets
192
+ for _ in range(args.datasets_per_batch):
193
+ valid_dataset = False #Check that datasets have the same 2/3 col format
194
+ while not valid_dataset:
195
+ data_idx = random.choice(dataset_indices)
196
+ if batch_format is None:
197
+ batch_format = num_cols[data_idx]
198
+ valid_dataset = True
199
+ else: #Check that this dataset has the same format
200
+ valid_dataset = (batch_format == num_cols[data_idx])
201
+
202
+ #Get data from this dataset
203
+ dataset = datasets[data_idx]
204
+ for _ in range(num_same_dataset):
205
+ for _ in range(args.nprocs):
206
+ batch_device = [] #A batch for one device
207
+ while len(batch_device) < args.batch_size:
208
+ sample = next(dataset)
209
+ in_batch = False
210
+ for text in sample:
211
+ if text in texts_in_batch:
212
+ in_batch = True
213
+ break
214
+
215
+ if not in_batch:
216
+ for text in sample:
217
+ texts_in_batch.add(text)
218
+ batch_device.append(sample)
219
+
220
+ queue.put(batch_device)
221
+
222
+
223
+ class RedditDataset:
224
+ """
225
+ A class that handles the reddit data files
226
+ """
227
+ def __init__(self, filepath):
228
+ self.filepath = filepath
229
+
230
+ def __iter__(self):
231
+ while True:
232
+ with gzip.open(self.filepath, "rt") as fIn:
233
+ for line in fIn:
234
+ data = json.loads(line)
235
+
236
+ if "response" in data and "context" in data:
237
+ yield [data["response"], data["context"]]
238
+
239
+ class Dataset:
240
+ """
241
+ A class that handles one dataset
242
+ """
243
+ def __init__(self, filepath):
244
+ self.filepath = filepath
245
+
246
+ def __iter__(self):
247
+ max_dataset_size = 10*1000*1000 #Cache small datasets in memory
248
+ dataset = []
249
+ data_format = None
250
+
251
+ while dataset is None or len(dataset) == 0:
252
+ with gzip.open(self.filepath, "rt") as fIn:
253
+ for line in fIn:
254
+ data = json.loads(line)
255
+ if isinstance(data, dict):
256
+ data = data['texts']
257
+
258
+ if data_format is None:
259
+ data_format = len(data)
260
+
261
+ #Ensure that all entries are of the same 2/3 col format
262
+ assert len(data) == data_format
263
+
264
+ if dataset is not None:
265
+ dataset.append(data)
266
+ if len(dataset) >= max_dataset_size:
267
+ dataset = None
268
+
269
+ yield data
270
+
271
+ # Data loaded. Now stream to the queue
272
+ # Shuffle for each epoch
273
+ while True:
274
+ random.shuffle(dataset)
275
+ for data in dataset:
276
+ yield data
277
+
278
+
279
+
280
+ if __name__ == "__main__":
281
+ parser = argparse.ArgumentParser()
282
+ parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased')
283
+ parser.add_argument('--steps', type=int, default=2000)
284
+ parser.add_argument('--save_steps', type=int, default=10000)
285
+ parser.add_argument('--batch_size', type=int, default=64)
286
+ parser.add_argument('--max_length', type=int, default=128)
287
+ parser.add_argument('--nprocs', type=int, default=8)
288
+ parser.add_argument('--datasets_per_batch', type=int, default=2, help="Number of datasets per batch")
289
+ parser.add_argument('--scale', type=float, default=20, help="Use 20 for cossim, and 1 when you work with unnormalized embeddings with dot product")
290
+ parser.add_argument('--data_folder', default="/data", help="Folder with your dataset files")
291
+ parser.add_argument('data_config', help="A data_config.json file")
292
+ parser.add_argument('output')
293
+ args = parser.parse_args()
294
+
295
+ # Ensure global batch size is divisble by data_sample_size
296
+ assert (args.batch_size*args.nprocs) % args.datasets_per_batch == 0
297
+
298
+ logging.info("Output: "+args.output)
299
+ if os.path.exists(args.output):
300
+ print("Output folder already exists.")
301
+ input("Continue?")
302
+
303
+ # Write train script to output path
304
+ os.makedirs(args.output, exist_ok=True)
305
+
306
+ data_config_path = os.path.join(args.output, 'data_config.json')
307
+ copyfile(args.data_config, data_config_path)
308
+
309
+ train_script_path = os.path.join(args.output, 'train_script.py')
310
+ copyfile(__file__, train_script_path)
311
+ with open(train_script_path, 'a') as fOut:
312
+ fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
313
+
314
+
315
+
316
+ #Load data config
317
+ with open(args.data_config) as fIn:
318
+ data_config = json.load(fIn)
319
+
320
+ queue = mp.Queue(maxsize=100*args.nprocs)
321
+
322
+ filepaths = []
323
+ dataset_indices = []
324
+ for idx, data in enumerate(data_config):
325
+ filepaths.append(os.path.join(os.path.expanduser(args.data_folder), data['name']))
326
+ dataset_indices.extend([idx]*data['weight'])
327
+
328
+ # Start producer
329
+ p = mp.Process(target=produce_data, args=(args, queue, filepaths, dataset_indices))
330
+ p.start()
331
+
332
+ # Run training
333
+ print("Start processes:", args.nprocs)
334
+ xmp.spawn(train_function, args=(args, queue), nprocs=args.nprocs, start_method='fork')
335
+ print("Training done")
336
+ print("It might be that not all processes exit automatically. In that case you must manually kill this process.")
337
+ print("With 'pkill python' you can kill all remaining python processes")
338
+ p.kill()
339
+ exit()
340
+
341
+
342
+
343
+ # Script was called via:
344
+ #python train_many_data_files_v2.py --steps 1000000 --batch_size 128 --model nreimers/MiniLM-L6-H384-uncased train_data_configs/all_datasets_v4.json output/all_datasets_v4_MiniLM-L6-H384-uncased-batch128
vocab.txt ADDED
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