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  ---
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  base_model: colorfulscoop/sbert-base-ja
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- library_name: sentence-transformers
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- metrics:
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- - accuracy
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- pipeline_tag: sentence-similarity
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- tags:
8
- - sentence-transformers
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- - sentence-similarity
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- - feature-extraction
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- - generated_from_trainer
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- - dataset_size:5330
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- - loss:SoftmaxLoss
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- widget:
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- - source_sentence: 顔 に マスク を かぶった 男性 は 、 測定 中 に 残した すべて の 作業 を 一 時 停止 して い ます 。
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- sentences:
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- - 誰 か が マスク を して いる 。
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- - 緑 の 服 を 着た 女性 が 、 別の 男性 の 顔 に 何 か を 書き ます 。
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- - 男性 が 女性 に キス して い ます 。
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- - source_sentence: 女の子 は 、 バレーボール を スパイク に ジャンプ し ます 。
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- sentences:
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- - ベンチ で 昼寝 を して いる 男
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- - プロパガンダ は 反戦 です 。
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- - 女の子 が ジャンプ し ます 。
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- - source_sentence: ステージ で ドラム を 演奏 する 男 。
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- sentences:
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- - 男 が ステージ で リズム を 作り ます 。
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- - 女性 は 頭 を 覆って いる
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- - 2 人 の 女性 が リビング ルーム に 座って レシピ を 議論 して い ます 。
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- - source_sentence: 青い シャツ を 着て フィールド を 耕す 東洋 の 帽子 を 持つ 男 。
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- sentences:
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- - 男 が 水 を サーフィン して いる
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- - 子犬 と ビーチ を 訪れた 人々
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- - バイク に 乗る 男
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- - source_sentence: 水着 姿 の 少女 に バケツ の 水 を 注ぐ ウォーター パーク の 少年 。
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- sentences:
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- - 騎士 たち は 、 溶けた 鉛 の バケツ を 城壁 の 下 の 不幸な 農奴 に 注ぎ ます 。
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- - 誰 も 歩いて い ない
39
- - 僧 ks は にぎやかな 通り を 渡り ます 。
40
- model-index:
41
- - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
42
- results:
43
- - task:
44
- type: label-accuracy
45
- name: Label Accuracy
46
- dataset:
47
- name: val
48
- type: val
49
- metrics:
50
- - type: accuracy
51
- value: 0.7782363977485929
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- name: Accuracy
53
  ---
54
 
55
- # SentenceTransformer based on colorfulscoop/sbert-base-ja
 
 
 
56
 
57
- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv dataset. 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.
58
 
59
  ## Model Details
60
 
61
  ### Model Description
62
- - **Model Type:** Sentence Transformer
63
- - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
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- - **Maximum Sequence Length:** 512 tokens
65
- - **Output Dimensionality:** 768 tokens
66
- - **Similarity Function:** Cosine Similarity
67
- - **Training Dataset:**
68
- - csv
69
- <!-- - **Language:** Unknown -->
70
- <!-- - **License:** Unknown -->
71
 
72
- ### Model Sources
73
 
74
- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
75
- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
76
- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
77
 
78
- ### Full Model Architecture
 
 
 
 
 
 
79
 
80
- ```
81
- SentenceTransformer(
82
- (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
83
- (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})
84
- )
85
- ```
86
 
87
- ## Usage
88
 
89
- ### Direct Usage (Sentence Transformers)
 
 
90
 
91
- First install the Sentence Transformers library:
92
 
93
- ```bash
94
- pip install -U sentence-transformers
95
- ```
96
 
97
- Then you can load this model and run inference.
98
- ```python
99
- from sentence_transformers import SentenceTransformer
100
 
101
- # Download from the 🤗 Hub
102
- model = SentenceTransformer("sentence_transformers_model_id")
103
- # Run inference
104
- sentences = [
105
- '水着 姿 の 少女 に バケツ の 水 を 注ぐ ウォーター パーク の 少年 。',
106
- '騎士 たち は 、 溶けた 鉛 の バケツ を 城壁 の 下 の 不幸な 農奴 に 注ぎ ます 。',
107
- '僧 ks は にぎやかな 通り を 渡り ます 。',
108
- ]
109
- embeddings = model.encode(sentences)
110
- print(embeddings.shape)
111
- # [3, 768]
112
 
113
- # Get the similarity scores for the embeddings
114
- similarities = model.similarity(embeddings, embeddings)
115
- print(similarities.shape)
116
- # [3, 3]
117
- ```
118
 
119
- <!--
120
- ### Direct Usage (Transformers)
121
 
122
- <details><summary>Click to see the direct usage in Transformers</summary>
123
 
124
- </details>
125
- -->
126
 
127
- <!--
128
- ### Downstream Usage (Sentence Transformers)
129
 
130
- You can finetune this model on your own dataset.
131
 
132
- <details><summary>Click to expand</summary>
133
 
134
- </details>
135
- -->
136
 
137
- <!--
138
- ### Out-of-Scope Use
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
 
140
- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
141
- -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
142
 
143
  ## Evaluation
144
 
145
- ### Metrics
146
 
147
- #### Label Accuracy
148
- * Dataset: `val`
149
- * Evaluated with [<code>LabelAccuracyEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.LabelAccuracyEvaluator)
150
 
151
- | Metric | Value |
152
- |:-------------|:-----------|
153
- | **accuracy** | **0.7782** |
154
 
155
- <!--
156
- ## Bias, Risks and Limitations
157
 
158
- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
159
- -->
160
 
161
- <!--
162
- ### Recommendations
163
 
164
- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
165
- -->
166
 
167
- ## Training Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
168
 
169
- ### Training Dataset
170
-
171
- #### csv
172
-
173
- * Dataset: csv
174
- * Size: 5,330 training samples
175
- * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
176
- * Approximate statistics based on the first 1000 samples:
177
- | | sentence_0 | sentence_1 | label |
178
- |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
179
- | type | string | string | int |
180
- | details | <ul><li>min: 7 tokens</li><li>mean: 35.79 tokens</li><li>max: 177 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 22.66 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>0: ~32.80%</li><li>1: ~67.20%</li></ul> |
181
- * Samples:
182
- | sentence_0 | sentence_1 | label |
183
- |:----------------------------------------------------------------------|:------------------------------------------|:---------------|
184
- | <code>薬剤 師 が 処方 を 準備 して い ます 。</code> | <code>薬剤 師 が 自宅 の ソファ に 座って い ます 。</code> | <code>1</code> |
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- | <code>3 人 の 男性 が 小屋 を 背景 に 象 に 乗って おり 、 2 人 が 帽子 を かぶって い ます 。</code> | <code>一 人 の 男 は 帽子 を かぶって い ませ ん 。</code> | <code>0</code> |
186
- | <code>茶色 の 犬 と の クロスカントリー スキー の 女性 。</code> | <code>草 は 緑 でした</code> | <code>1</code> |
187
- * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
188
-
189
- ### Evaluation Dataset
190
-
191
- #### csv
192
-
193
- * Dataset: csv
194
- * Size: 5,330 evaluation samples
195
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
196
- * Approximate statistics based on the first 1000 samples:
197
- | | text1 | text2 | label |
198
- |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
199
- | type | string | string | int |
200
- | details | <ul><li>min: 12 tokens</li><li>mean: 36.61 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.81 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>0: ~32.90%</li><li>1: ~67.10%</li></ul> |
201
- * Samples:
202
- | text1 | text2 | label |
203
- |:----------------------------------------------------------------------------|:---------------------------------------------------------|:---------------|
204
- | <code>青い ジャージ の 裏 に 10 番 の ソフトボール プレーヤー が ホーム プレート に 向かって 走って い ます 。</code> | <code>ソフトボール 選手 は 10 番 です</code> | <code>0</code> |
205
- | <code>山 の 湖 の そば の 岩 だらけ の 道 で 自転車 に 乗る 男 。</code> | <code>自転車 の 男</code> | <code>0</code> |
206
- | <code>テント の 前 の 芝生 の 椅子 に 座って いる 赤い ひげ を 生やした ひげ を 生やした 男性 。</code> | <code>顔 の 毛 の ない 男性 と 青い シャツ を 着た 女性 が 座って い ます 。</code> | <code>1</code> |
207
- * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
208
-
209
- ### Training Hyperparameters
210
- #### Non-Default Hyperparameters
211
-
212
- - `eval_strategy`: steps
213
- - `num_train_epochs`: 1
214
- - `multi_dataset_batch_sampler`: round_robin
215
-
216
- #### All Hyperparameters
217
- <details><summary>Click to expand</summary>
218
-
219
- - `overwrite_output_dir`: False
220
- - `do_predict`: False
221
- - `eval_strategy`: steps
222
- - `prediction_loss_only`: True
223
- - `per_device_train_batch_size`: 8
224
- - `per_device_eval_batch_size`: 8
225
- - `per_gpu_train_batch_size`: None
226
- - `per_gpu_eval_batch_size`: None
227
- - `gradient_accumulation_steps`: 1
228
- - `eval_accumulation_steps`: None
229
- - `torch_empty_cache_steps`: None
230
- - `learning_rate`: 5e-05
231
- - `weight_decay`: 0.0
232
- - `adam_beta1`: 0.9
233
- - `adam_beta2`: 0.999
234
- - `adam_epsilon`: 1e-08
235
- - `max_grad_norm`: 1
236
- - `num_train_epochs`: 1
237
- - `max_steps`: -1
238
- - `lr_scheduler_type`: linear
239
- - `lr_scheduler_kwargs`: {}
240
- - `warmup_ratio`: 0.0
241
- - `warmup_steps`: 0
242
- - `log_level`: passive
243
- - `log_level_replica`: warning
244
- - `log_on_each_node`: True
245
- - `logging_nan_inf_filter`: True
246
- - `save_safetensors`: True
247
- - `save_on_each_node`: False
248
- - `save_only_model`: False
249
- - `restore_callback_states_from_checkpoint`: False
250
- - `no_cuda`: False
251
- - `use_cpu`: False
252
- - `use_mps_device`: False
253
- - `seed`: 42
254
- - `data_seed`: None
255
- - `jit_mode_eval`: False
256
- - `use_ipex`: False
257
- - `bf16`: False
258
- - `fp16`: False
259
- - `fp16_opt_level`: O1
260
- - `half_precision_backend`: auto
261
- - `bf16_full_eval`: False
262
- - `fp16_full_eval`: False
263
- - `tf32`: None
264
- - `local_rank`: 0
265
- - `ddp_backend`: None
266
- - `tpu_num_cores`: None
267
- - `tpu_metrics_debug`: False
268
- - `debug`: []
269
- - `dataloader_drop_last`: False
270
- - `dataloader_num_workers`: 0
271
- - `dataloader_prefetch_factor`: None
272
- - `past_index`: -1
273
- - `disable_tqdm`: False
274
- - `remove_unused_columns`: True
275
- - `label_names`: None
276
- - `load_best_model_at_end`: False
277
- - `ignore_data_skip`: False
278
- - `fsdp`: []
279
- - `fsdp_min_num_params`: 0
280
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
281
- - `fsdp_transformer_layer_cls_to_wrap`: None
282
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
283
- - `deepspeed`: None
284
- - `label_smoothing_factor`: 0.0
285
- - `optim`: adamw_torch
286
- - `optim_args`: None
287
- - `adafactor`: False
288
- - `group_by_length`: False
289
- - `length_column_name`: length
290
- - `ddp_find_unused_parameters`: None
291
- - `ddp_bucket_cap_mb`: None
292
- - `ddp_broadcast_buffers`: False
293
- - `dataloader_pin_memory`: True
294
- - `dataloader_persistent_workers`: False
295
- - `skip_memory_metrics`: True
296
- - `use_legacy_prediction_loop`: False
297
- - `push_to_hub`: False
298
- - `resume_from_checkpoint`: None
299
- - `hub_model_id`: None
300
- - `hub_strategy`: every_save
301
- - `hub_private_repo`: False
302
- - `hub_always_push`: False
303
- - `gradient_checkpointing`: False
304
- - `gradient_checkpointing_kwargs`: None
305
- - `include_inputs_for_metrics`: False
306
- - `eval_do_concat_batches`: True
307
- - `fp16_backend`: auto
308
- - `push_to_hub_model_id`: None
309
- - `push_to_hub_organization`: None
310
- - `mp_parameters`:
311
- - `auto_find_batch_size`: False
312
- - `full_determinism`: False
313
- - `torchdynamo`: None
314
- - `ray_scope`: last
315
- - `ddp_timeout`: 1800
316
- - `torch_compile`: False
317
- - `torch_compile_backend`: None
318
- - `torch_compile_mode`: None
319
- - `dispatch_batches`: None
320
- - `split_batches`: None
321
- - `include_tokens_per_second`: False
322
- - `include_num_input_tokens_seen`: False
323
- - `neftune_noise_alpha`: None
324
- - `optim_target_modules`: None
325
- - `batch_eval_metrics`: False
326
- - `eval_on_start`: False
327
- - `eval_use_gather_object`: False
328
- - `batch_sampler`: batch_sampler
329
- - `multi_dataset_batch_sampler`: round_robin
330
-
331
- </details>
332
-
333
- ### Training Logs
334
- | Epoch | Step | val_accuracy |
335
- |:------:|:----:|:------------:|
336
- | 0.1497 | 50 | 0.7265 |
337
- | 0.2994 | 100 | 0.7696 |
338
- | 0.4491 | 150 | 0.7715 |
339
- | 0.5988 | 200 | 0.7659 |
340
- | 0.7485 | 250 | 0.7790 |
341
- | 0.8982 | 300 | 0.7771 |
342
- | 1.0 | 334 | 0.7782 |
343
-
344
-
345
- ### Framework Versions
346
- - Python: 3.10.14
347
- - Sentence Transformers: 3.1.0
348
- - Transformers: 4.44.2
349
- - PyTorch: 2.4.1+cu121
350
- - Accelerate: 0.34.2
351
- - Datasets: 2.20.0
352
- - Tokenizers: 0.19.1
353
-
354
- ## Citation
355
-
356
- ### BibTeX
357
-
358
- #### Sentence Transformers and SoftmaxLoss
359
- ```bibtex
360
- @inproceedings{reimers-2019-sentence-bert,
361
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
362
- author = "Reimers, Nils and Gurevych, Iryna",
363
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
364
- month = "11",
365
- year = "2019",
366
- publisher = "Association for Computational Linguistics",
367
- url = "https://arxiv.org/abs/1908.10084",
368
- }
369
- ```
370
-
371
- <!--
372
- ## Glossary
373
-
374
- *Clearly define terms in order to be accessible across audiences.*
375
- -->
376
-
377
- <!--
378
- ## Model Card Authors
379
-
380
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
381
- -->
382
-
383
- <!--
384
  ## Model Card Contact
385
 
386
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
387
- -->
 
1
  ---
2
  base_model: colorfulscoop/sbert-base-ja
3
+ language: ja
4
+ license: cc-by-sa-4.0
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+ model_name: LeoChiuu/sbert-base-ja-arc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
  ---
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8
+ # Model Card for LeoChiuu/sbert-base-ja-arc
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+
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+ <!-- 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
+ Generates similarity embeddings
 
 
21
 
22
+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** ja
27
+ - **License:** cc-by-sa-4.0
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+ - **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
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30
+ ### Model Sources [optional]
 
 
 
 
 
31
 
32
+ <!-- Provide the basic links for the model. -->
33
 
34
+ - **Repository:** [More Information Needed]
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
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+
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
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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  ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ ### Testing Data, Factors & Metrics
 
 
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+ #### Testing Data
 
 
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+ <!-- This should link to a Dataset Card if possible. -->
 
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+ [More Information Needed]
 
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+ #### Factors
 
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ [More Information Needed]
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+
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+ ### Results
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ 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).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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  ## Model Card Contact
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+ [More Information Needed]