Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
@@ -1,362 +1,201 @@
|
|
1 |
---
|
2 |
-
|
3 |
-
|
4 |
-
|
5 |
-
|
6 |
-
metrics:
|
7 |
-
- accuracy
|
8 |
-
pipeline_tag: sentence-similarity
|
9 |
-
tags:
|
10 |
-
- sentence-transformers
|
11 |
-
- sentence-similarity
|
12 |
-
- feature-extraction
|
13 |
-
- generated_from_trainer
|
14 |
-
- dataset_size:124
|
15 |
-
- loss:SoftmaxLoss
|
16 |
-
widget:
|
17 |
-
- source_sentence: あの木の上のやつ、スカーフ?
|
18 |
-
sentences:
|
19 |
-
- あの木の上の布はなに?
|
20 |
-
- うん探そう
|
21 |
-
- なにも要らない
|
22 |
-
- source_sentence: スカーフは布袋の中?
|
23 |
-
sentences:
|
24 |
-
- 棚にトマトが見当たらないから
|
25 |
-
- スカーフは布袋にある?
|
26 |
-
- ロウソク
|
27 |
-
- source_sentence: ためすため
|
28 |
-
sentences:
|
29 |
-
- 水を井戸からくんでいた
|
30 |
-
- どのくらいのサイズ?
|
31 |
-
- 自分を試すため
|
32 |
-
- source_sentence: レオが夜当番だから
|
33 |
-
sentences:
|
34 |
-
- 暖炉にスカーフを置いた?
|
35 |
-
- 夜当番だから
|
36 |
-
- スカーフはジョウロの中にある?
|
37 |
-
- source_sentence: 窓から飛んで行った
|
38 |
-
sentences:
|
39 |
-
- 窓が開いていたから
|
40 |
-
- オブリビオンの魔法
|
41 |
-
- 窓から出て行った
|
42 |
-
model-index:
|
43 |
-
- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
|
44 |
-
results:
|
45 |
-
- task:
|
46 |
-
type: label-accuracy
|
47 |
-
name: Label Accuracy
|
48 |
-
dataset:
|
49 |
-
name: val
|
50 |
-
type: val
|
51 |
-
metrics:
|
52 |
-
- type: accuracy
|
53 |
-
value: 1.0
|
54 |
-
name: Accuracy
|
55 |
---
|
56 |
|
57 |
-
#
|
|
|
|
|
|
|
58 |
|
59 |
-
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
60 |
|
61 |
## Model Details
|
62 |
|
63 |
### Model Description
|
64 |
-
- **Model Type:** Sentence Transformer
|
65 |
-
- **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
|
66 |
-
- **Maximum Sequence Length:** 512 tokens
|
67 |
-
- **Output Dimensionality:** 768 tokens
|
68 |
-
- **Similarity Function:** Cosine Similarity
|
69 |
-
<!-- - **Training Dataset:** Unknown -->
|
70 |
-
<!-- - **Language:** Unknown -->
|
71 |
-
<!-- - **License:** Unknown -->
|
72 |
|
73 |
-
|
74 |
|
75 |
-
|
76 |
-
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
77 |
-
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
78 |
|
79 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
80 |
|
81 |
-
|
82 |
-
SentenceTransformer(
|
83 |
-
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
84 |
-
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
85 |
-
)
|
86 |
-
```
|
87 |
|
88 |
-
|
89 |
|
90 |
-
|
|
|
|
|
91 |
|
92 |
-
|
93 |
|
94 |
-
|
95 |
-
pip install -U sentence-transformers
|
96 |
-
```
|
97 |
|
98 |
-
|
99 |
-
```python
|
100 |
-
from sentence_transformers import SentenceTransformer
|
101 |
|
102 |
-
|
103 |
-
model = SentenceTransformer("LeoChiuu/sbert-base-ja")
|
104 |
-
# Run inference
|
105 |
-
sentences = [
|
106 |
-
'窓から飛んで行った',
|
107 |
-
'窓から出て行った',
|
108 |
-
'オブリビオンの魔法',
|
109 |
-
]
|
110 |
-
embeddings = model.encode(sentences)
|
111 |
-
print(embeddings.shape)
|
112 |
-
# [3, 768]
|
113 |
|
114 |
-
|
115 |
-
similarities = model.similarity(embeddings, embeddings)
|
116 |
-
print(similarities.shape)
|
117 |
-
# [3, 3]
|
118 |
-
```
|
119 |
|
120 |
-
|
121 |
-
### Direct Usage (Transformers)
|
122 |
|
123 |
-
|
124 |
|
125 |
-
|
126 |
-
-->
|
127 |
|
128 |
-
|
129 |
-
### Downstream Usage (Sentence Transformers)
|
130 |
|
131 |
-
|
132 |
|
133 |
-
|
134 |
|
135 |
-
|
136 |
-
-->
|
137 |
|
138 |
-
<!--
|
139 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
140 |
|
141 |
-
|
142 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
143 |
|
144 |
## Evaluation
|
145 |
|
146 |
-
|
147 |
|
148 |
-
|
149 |
-
* Dataset: `val`
|
150 |
-
* Evaluated with [<code>LabelAccuracyEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.LabelAccuracyEvaluator)
|
151 |
|
152 |
-
|
153 |
-
|:-------------|:--------|
|
154 |
-
| **accuracy** | **1.0** |
|
155 |
|
156 |
-
<!--
|
157 |
-
## Bias, Risks and Limitations
|
158 |
|
159 |
-
|
160 |
-
-->
|
161 |
|
162 |
-
|
163 |
-
### Recommendations
|
164 |
|
165 |
-
|
166 |
-
-->
|
167 |
|
168 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
169 |
|
170 |
-
### Training Dataset
|
171 |
-
|
172 |
-
#### Unnamed Dataset
|
173 |
-
|
174 |
-
|
175 |
-
* Size: 124 training samples
|
176 |
-
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
|
177 |
-
* Approximate statistics based on the first 1000 samples:
|
178 |
-
| | sentence_0 | sentence_1 | label |
|
179 |
-
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
|
180 |
-
| type | string | string | int |
|
181 |
-
| details | <ul><li>min: 4 tokens</li><li>mean: 8.59 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 8.58 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
|
182 |
-
* Samples:
|
183 |
-
| sentence_0 | sentence_1 | label |
|
184 |
-
|:-------------------------|:-----------------------|:---------------|
|
185 |
-
| <code>家の中</code> | <code>家の中へ行こう</code> | <code>1</code> |
|
186 |
-
| <code>夕ご飯は何を食べたの?</code> | <code>昨晩何を食べたの?</code> | <code>1</code> |
|
187 |
-
| <code>井戸を使っていた</code> | <code>井戸を使った</code> | <code>1</code> |
|
188 |
-
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
|
189 |
-
|
190 |
-
### Training Hyperparameters
|
191 |
-
#### Non-Default Hyperparameters
|
192 |
-
|
193 |
-
- `eval_strategy`: steps
|
194 |
-
- `num_train_epochs`: 1
|
195 |
-
- `multi_dataset_batch_sampler`: round_robin
|
196 |
-
|
197 |
-
#### All Hyperparameters
|
198 |
-
<details><summary>Click to expand</summary>
|
199 |
-
|
200 |
-
- `overwrite_output_dir`: False
|
201 |
-
- `do_predict`: False
|
202 |
-
- `eval_strategy`: steps
|
203 |
-
- `prediction_loss_only`: True
|
204 |
-
- `per_device_train_batch_size`: 8
|
205 |
-
- `per_device_eval_batch_size`: 8
|
206 |
-
- `per_gpu_train_batch_size`: None
|
207 |
-
- `per_gpu_eval_batch_size`: None
|
208 |
-
- `gradient_accumulation_steps`: 1
|
209 |
-
- `eval_accumulation_steps`: None
|
210 |
-
- `torch_empty_cache_steps`: None
|
211 |
-
- `learning_rate`: 5e-05
|
212 |
-
- `weight_decay`: 0.0
|
213 |
-
- `adam_beta1`: 0.9
|
214 |
-
- `adam_beta2`: 0.999
|
215 |
-
- `adam_epsilon`: 1e-08
|
216 |
-
- `max_grad_norm`: 1
|
217 |
-
- `num_train_epochs`: 1
|
218 |
-
- `max_steps`: -1
|
219 |
-
- `lr_scheduler_type`: linear
|
220 |
-
- `lr_scheduler_kwargs`: {}
|
221 |
-
- `warmup_ratio`: 0.0
|
222 |
-
- `warmup_steps`: 0
|
223 |
-
- `log_level`: passive
|
224 |
-
- `log_level_replica`: warning
|
225 |
-
- `log_on_each_node`: True
|
226 |
-
- `logging_nan_inf_filter`: True
|
227 |
-
- `save_safetensors`: True
|
228 |
-
- `save_on_each_node`: False
|
229 |
-
- `save_only_model`: False
|
230 |
-
- `restore_callback_states_from_checkpoint`: False
|
231 |
-
- `no_cuda`: False
|
232 |
-
- `use_cpu`: False
|
233 |
-
- `use_mps_device`: False
|
234 |
-
- `seed`: 42
|
235 |
-
- `data_seed`: None
|
236 |
-
- `jit_mode_eval`: False
|
237 |
-
- `use_ipex`: False
|
238 |
-
- `bf16`: False
|
239 |
-
- `fp16`: False
|
240 |
-
- `fp16_opt_level`: O1
|
241 |
-
- `half_precision_backend`: auto
|
242 |
-
- `bf16_full_eval`: False
|
243 |
-
- `fp16_full_eval`: False
|
244 |
-
- `tf32`: None
|
245 |
-
- `local_rank`: 0
|
246 |
-
- `ddp_backend`: None
|
247 |
-
- `tpu_num_cores`: None
|
248 |
-
- `tpu_metrics_debug`: False
|
249 |
-
- `debug`: []
|
250 |
-
- `dataloader_drop_last`: False
|
251 |
-
- `dataloader_num_workers`: 0
|
252 |
-
- `dataloader_prefetch_factor`: None
|
253 |
-
- `past_index`: -1
|
254 |
-
- `disable_tqdm`: False
|
255 |
-
- `remove_unused_columns`: True
|
256 |
-
- `label_names`: None
|
257 |
-
- `load_best_model_at_end`: False
|
258 |
-
- `ignore_data_skip`: False
|
259 |
-
- `fsdp`: []
|
260 |
-
- `fsdp_min_num_params`: 0
|
261 |
-
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
262 |
-
- `fsdp_transformer_layer_cls_to_wrap`: None
|
263 |
-
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
264 |
-
- `deepspeed`: None
|
265 |
-
- `label_smoothing_factor`: 0.0
|
266 |
-
- `optim`: adamw_torch
|
267 |
-
- `optim_args`: None
|
268 |
-
- `adafactor`: False
|
269 |
-
- `group_by_length`: False
|
270 |
-
- `length_column_name`: length
|
271 |
-
- `ddp_find_unused_parameters`: None
|
272 |
-
- `ddp_bucket_cap_mb`: None
|
273 |
-
- `ddp_broadcast_buffers`: False
|
274 |
-
- `dataloader_pin_memory`: True
|
275 |
-
- `dataloader_persistent_workers`: False
|
276 |
-
- `skip_memory_metrics`: True
|
277 |
-
- `use_legacy_prediction_loop`: False
|
278 |
-
- `push_to_hub`: False
|
279 |
-
- `resume_from_checkpoint`: None
|
280 |
-
- `hub_model_id`: None
|
281 |
-
- `hub_strategy`: every_save
|
282 |
-
- `hub_private_repo`: False
|
283 |
-
- `hub_always_push`: False
|
284 |
-
- `gradient_checkpointing`: False
|
285 |
-
- `gradient_checkpointing_kwargs`: None
|
286 |
-
- `include_inputs_for_metrics`: False
|
287 |
-
- `eval_do_concat_batches`: True
|
288 |
-
- `fp16_backend`: auto
|
289 |
-
- `push_to_hub_model_id`: None
|
290 |
-
- `push_to_hub_organization`: None
|
291 |
-
- `mp_parameters`:
|
292 |
-
- `auto_find_batch_size`: False
|
293 |
-
- `full_determinism`: False
|
294 |
-
- `torchdynamo`: None
|
295 |
-
- `ray_scope`: last
|
296 |
-
- `ddp_timeout`: 1800
|
297 |
-
- `torch_compile`: False
|
298 |
-
- `torch_compile_backend`: None
|
299 |
-
- `torch_compile_mode`: None
|
300 |
-
- `dispatch_batches`: None
|
301 |
-
- `split_batches`: None
|
302 |
-
- `include_tokens_per_second`: False
|
303 |
-
- `include_num_input_tokens_seen`: False
|
304 |
-
- `neftune_noise_alpha`: None
|
305 |
-
- `optim_target_modules`: None
|
306 |
-
- `batch_eval_metrics`: False
|
307 |
-
- `eval_on_start`: False
|
308 |
-
- `eval_use_gather_object`: False
|
309 |
-
- `batch_sampler`: batch_sampler
|
310 |
-
- `multi_dataset_batch_sampler`: round_robin
|
311 |
-
|
312 |
-
</details>
|
313 |
-
|
314 |
-
### Training Logs
|
315 |
-
| Epoch | Step | val_accuracy |
|
316 |
-
|:-----:|:----:|:------------:|
|
317 |
-
| 1.0 | 16 | 1.0 |
|
318 |
-
|
319 |
-
|
320 |
-
### Framework Versions
|
321 |
-
- Python: 3.11.9
|
322 |
-
- Sentence Transformers: 3.0.1
|
323 |
-
- Transformers: 4.44.1
|
324 |
-
- PyTorch: 2.3.0+cpu
|
325 |
-
- Accelerate: 0.32.1
|
326 |
-
- Datasets: 2.19.1
|
327 |
-
- Tokenizers: 0.19.1
|
328 |
-
|
329 |
-
## Citation
|
330 |
-
|
331 |
-
### BibTeX
|
332 |
-
|
333 |
-
#### Sentence Transformers and SoftmaxLoss
|
334 |
-
```bibtex
|
335 |
-
@inproceedings{reimers-2019-sentence-bert,
|
336 |
-
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
337 |
-
author = "Reimers, Nils and Gurevych, Iryna",
|
338 |
-
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
339 |
-
month = "11",
|
340 |
-
year = "2019",
|
341 |
-
publisher = "Association for Computational Linguistics",
|
342 |
-
url = "https://arxiv.org/abs/1908.10084",
|
343 |
-
}
|
344 |
-
```
|
345 |
-
|
346 |
-
<!--
|
347 |
-
## Glossary
|
348 |
-
|
349 |
-
*Clearly define terms in order to be accessible across audiences.*
|
350 |
-
-->
|
351 |
-
|
352 |
-
<!--
|
353 |
-
## Model Card Authors
|
354 |
-
|
355 |
-
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
356 |
-
-->
|
357 |
-
|
358 |
-
<!--
|
359 |
## Model Card Contact
|
360 |
|
361 |
-
|
362 |
-
-->
|
|
|
1 |
---
|
2 |
+
datasets: custom-data
|
3 |
+
language: en
|
4 |
+
license: apache-2.0
|
5 |
+
model_name: LeoChiuu/sbert-base-ja
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
---
|
7 |
|
8 |
+
# Model Card for LeoChiuu/sbert-base-ja
|
9 |
+
|
10 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
11 |
+
|
12 |
|
|
|
13 |
|
14 |
## Model Details
|
15 |
|
16 |
### Model Description
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
17 |
|
18 |
+
<!-- Provide a longer summary of what this model is. -->
|
19 |
|
20 |
+
Binary classification of sentences
|
|
|
|
|
21 |
|
22 |
+
- **Developed by:** [More Information Needed]
|
23 |
+
- **Funded by [optional]:** [More Information Needed]
|
24 |
+
- **Shared by [optional]:** [More Information Needed]
|
25 |
+
- **Model type:** [More Information Needed]
|
26 |
+
- **Language(s) (NLP):** en
|
27 |
+
- **License:** apache-2.0
|
28 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
29 |
|
30 |
+
### Model Sources [optional]
|
|
|
|
|
|
|
|
|
|
|
31 |
|
32 |
+
<!-- Provide the basic links for the model. -->
|
33 |
|
34 |
+
- **Repository:** https://github.com/huggingface/huggingface_hub
|
35 |
+
- **Paper [optional]:** [More Information Needed]
|
36 |
+
- **Demo [optional]:** [More Information Needed]
|
37 |
|
38 |
+
## Uses
|
39 |
|
40 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
|
|
|
|
41 |
|
42 |
+
### Direct Use
|
|
|
|
|
43 |
|
44 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
45 |
|
46 |
+
[More Information Needed]
|
|
|
|
|
|
|
|
|
47 |
|
48 |
+
### Downstream Use [optional]
|
|
|
49 |
|
50 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
51 |
|
52 |
+
[More Information Needed]
|
|
|
53 |
|
54 |
+
### Out-of-Scope Use
|
|
|
55 |
|
56 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
57 |
|
58 |
+
[More Information Needed]
|
59 |
|
60 |
+
## Bias, Risks, and Limitations
|
|
|
61 |
|
62 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
63 |
+
|
64 |
+
[More Information Needed]
|
65 |
+
|
66 |
+
### Recommendations
|
67 |
+
|
68 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
69 |
+
|
70 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
71 |
+
|
72 |
+
## How to Get Started with the Model
|
73 |
+
|
74 |
+
Use the code below to get started with the model.
|
75 |
+
|
76 |
+
[More Information Needed]
|
77 |
+
|
78 |
+
## Training Details
|
79 |
+
|
80 |
+
### Training Data
|
81 |
+
|
82 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
83 |
+
|
84 |
+
[More Information Needed]
|
85 |
|
86 |
+
### Training Procedure
|
87 |
+
|
88 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
89 |
+
|
90 |
+
#### Preprocessing [optional]
|
91 |
+
|
92 |
+
[More Information Needed]
|
93 |
+
|
94 |
+
|
95 |
+
#### Training Hyperparameters
|
96 |
+
|
97 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
98 |
+
|
99 |
+
#### Speeds, Sizes, Times [optional]
|
100 |
+
|
101 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
102 |
+
|
103 |
+
[More Information Needed]
|
104 |
|
105 |
## Evaluation
|
106 |
|
107 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
108 |
|
109 |
+
### Testing Data, Factors & Metrics
|
|
|
|
|
110 |
|
111 |
+
#### Testing Data
|
|
|
|
|
112 |
|
113 |
+
<!-- This should link to a Dataset Card if possible. -->
|
|
|
114 |
|
115 |
+
[More Information Needed]
|
|
|
116 |
|
117 |
+
#### Factors
|
|
|
118 |
|
119 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
|
|
120 |
|
121 |
+
[More Information Needed]
|
122 |
+
|
123 |
+
#### Metrics
|
124 |
+
|
125 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
126 |
+
|
127 |
+
[More Information Needed]
|
128 |
+
|
129 |
+
### Results
|
130 |
+
|
131 |
+
[More Information Needed]
|
132 |
+
|
133 |
+
#### Summary
|
134 |
+
|
135 |
+
|
136 |
+
|
137 |
+
## Model Examination [optional]
|
138 |
+
|
139 |
+
<!-- Relevant interpretability work for the model goes here -->
|
140 |
+
|
141 |
+
[More Information Needed]
|
142 |
+
|
143 |
+
## Environmental Impact
|
144 |
+
|
145 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
146 |
+
|
147 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
148 |
+
|
149 |
+
- **Hardware Type:** [More Information Needed]
|
150 |
+
- **Hours used:** [More Information Needed]
|
151 |
+
- **Cloud Provider:** [More Information Needed]
|
152 |
+
- **Compute Region:** [More Information Needed]
|
153 |
+
- **Carbon Emitted:** [More Information Needed]
|
154 |
+
|
155 |
+
## Technical Specifications [optional]
|
156 |
+
|
157 |
+
### Model Architecture and Objective
|
158 |
+
|
159 |
+
[More Information Needed]
|
160 |
+
|
161 |
+
### Compute Infrastructure
|
162 |
+
|
163 |
+
[More Information Needed]
|
164 |
+
|
165 |
+
#### Hardware
|
166 |
+
|
167 |
+
[More Information Needed]
|
168 |
+
|
169 |
+
#### Software
|
170 |
+
|
171 |
+
[More Information Needed]
|
172 |
+
|
173 |
+
## Citation [optional]
|
174 |
+
|
175 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
176 |
+
|
177 |
+
**BibTeX:**
|
178 |
+
|
179 |
+
[More Information Needed]
|
180 |
+
|
181 |
+
**APA:**
|
182 |
+
|
183 |
+
[More Information Needed]
|
184 |
+
|
185 |
+
## Glossary [optional]
|
186 |
+
|
187 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
188 |
+
|
189 |
+
[More Information Needed]
|
190 |
+
|
191 |
+
## More Information [optional]
|
192 |
+
|
193 |
+
[More Information Needed]
|
194 |
+
|
195 |
+
## Model Card Authors [optional]
|
196 |
+
|
197 |
+
[More Information Needed]
|
198 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
199 |
## Model Card Contact
|
200 |
|
201 |
+
[More Information Needed]
|
|