bicolino34 commited on
Commit
1a3e1e3
·
verified ·
1 Parent(s): 498d40e

Add new SentenceTransformer model

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": true,
4
+ "pooling_mode_mean_tokens": false,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
2_Dense/config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
2_Dense/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:42917cc65b3f155dfc4ef8d0c79934eb7a71eb757d80b457715f707931200b90
3
+ size 2362528
README.md ADDED
@@ -0,0 +1,413 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:13304
8
+ - loss:MultipleNegativesRankingLoss
9
+ base_model: sentence-transformers/LaBSE
10
+ widget:
11
+ - source_sentence: それは彼女のアリバイになるはずだ。
12
+ sentences:
13
+ - Важко сказати.
14
+ - Це мало правити їй за алібі.
15
+ - — Ні.
16
+ - source_sentence: 声が上機嫌になった。
17
+ sentences:
18
+ - Фукаері кивнула.
19
+ - 'Його голос став веселішим:'
20
+ - Бо карлики більше полюбляли природну дощову воду, ніж річкову.
21
+ - source_sentence: 天吾は前夜、長い時間をかけて知恵を絞り、それを作成したのだ。
22
+ sentences:
23
+ - Повернути назад куплений товар і взяти новий не випадає.
24
+ - «Погратися з наручниками?» — подумала вона.
25
+ - Минулого вечора він довго сушив собі голову над ними.
26
+ - source_sentence: 「その人たちにどんなことをされたの?」
27
+ sentences:
28
+ - — Правду кажучи, я до двадцяти років залишалася незайманою.
29
+ - Та все одно я кохала його.
30
+ - — І до чого вони вас примушували?
31
+ - source_sentence: 微かな、しかし打ち消しがたい違和感がそこにはある。
32
+ sentences:
33
+ - Якась легка, але незаперечна відмінність.
34
+ - Кожна людина вільна обирати, як їй жити.
35
+ - Дуже дякую!
36
+ pipeline_tag: sentence-similarity
37
+ library_name: sentence-transformers
38
+ ---
39
+
40
+ # SentenceTransformer based on sentence-transformers/LaBSE
41
+
42
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) 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.
43
+
44
+ ## Model Details
45
+
46
+ ### Model Description
47
+ - **Model Type:** Sentence Transformer
48
+ - **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision b7f947194ceae0ddf90bafe213722569e274ad28 -->
49
+ - **Maximum Sequence Length:** 256 tokens
50
+ - **Output Dimensionality:** 768 dimensions
51
+ - **Similarity Function:** Cosine Similarity
52
+ - **Training Dataset:**
53
+ - csv
54
+ <!-- - **Language:** Unknown -->
55
+ <!-- - **License:** Unknown -->
56
+
57
+ ### Model Sources
58
+
59
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
60
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
61
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
62
+
63
+ ### Full Model Architecture
64
+
65
+ ```
66
+ SentenceTransformer(
67
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
68
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
69
+ (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
70
+ (3): Normalize()
71
+ )
72
+ ```
73
+
74
+ ## Usage
75
+
76
+ ### Direct Usage (Sentence Transformers)
77
+
78
+ First install the Sentence Transformers library:
79
+
80
+ ```bash
81
+ pip install -U sentence-transformers
82
+ ```
83
+
84
+ Then you can load this model and run inference.
85
+ ```python
86
+ from sentence_transformers import SentenceTransformer
87
+
88
+ # Download from the 🤗 Hub
89
+ model = SentenceTransformer("bicolino34/LaBSE-ja-uk")
90
+ # Run inference
91
+ sentences = [
92
+ '微かな、しかし打ち消しがたい違和感がそこにはある。',
93
+ 'Якась легка, але незаперечна відмінність.',
94
+ 'Кожна людина вільна обирати, як їй жити.',
95
+ ]
96
+ embeddings = model.encode(sentences)
97
+ print(embeddings.shape)
98
+ # [3, 768]
99
+
100
+ # Get the similarity scores for the embeddings
101
+ similarities = model.similarity(embeddings, embeddings)
102
+ print(similarities.shape)
103
+ # [3, 3]
104
+ ```
105
+
106
+ <!--
107
+ ### Direct Usage (Transformers)
108
+
109
+ <details><summary>Click to see the direct usage in Transformers</summary>
110
+
111
+ </details>
112
+ -->
113
+
114
+ <!--
115
+ ### Downstream Usage (Sentence Transformers)
116
+
117
+ You can finetune this model on your own dataset.
118
+
119
+ <details><summary>Click to expand</summary>
120
+
121
+ </details>
122
+ -->
123
+
124
+ <!--
125
+ ### Out-of-Scope Use
126
+
127
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
128
+ -->
129
+
130
+ <!--
131
+ ## Bias, Risks and Limitations
132
+
133
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
134
+ -->
135
+
136
+ <!--
137
+ ### Recommendations
138
+
139
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
140
+ -->
141
+
142
+ ## Training Details
143
+
144
+ ### Training Dataset
145
+
146
+ #### csv
147
+
148
+ * Dataset: csv
149
+ * Size: 13,304 training samples
150
+ * Columns: <code>Source</code> and <code>Target</code>
151
+ * Approximate statistics based on the first 1000 samples:
152
+ | | Source | Target |
153
+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
154
+ | type | string | string |
155
+ | details | <ul><li>min: 4 tokens</li><li>mean: 22.68 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 19.39 tokens</li><li>max: 93 tokens</li></ul> |
156
+ * Samples:
157
+ | Source | Target |
158
+ |:--------------------------------------------------|:-------------------------------------------------------------------------------|
159
+ | <code>あたりはまだ暗い。</code> | <code>Навколо все ще було темно.</code> |
160
+ | <code>しかし受話器をとるものはいない。</code> | <code>Однак ніхто не підніме слухавки.</code> |
161
+ | <code>前にも言ったように、深田は宗教的な傾向など露ほども持ちあわせない人物だ。</code> | <code>Як я казав раніше, Фукада не мав найменшої схильності до релігії.</code> |
162
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
163
+ ```json
164
+ {
165
+ "scale": 20.0,
166
+ "similarity_fct": "cos_sim"
167
+ }
168
+ ```
169
+
170
+ ### Evaluation Dataset
171
+
172
+ #### csv
173
+
174
+ * Dataset: csv
175
+ * Size: 13,304 evaluation samples
176
+ * Columns: <code>Source</code> and <code>Target</code>
177
+ * Approximate statistics based on the first 1000 samples:
178
+ | | Source | Target |
179
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
180
+ | type | string | string |
181
+ | details | <ul><li>min: 4 tokens</li><li>mean: 21.78 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 19.04 tokens</li><li>max: 72 tokens</li></ul> |
182
+ * Samples:
183
+ | Source | Target |
184
+ |:--------------------------------|:--------------------------------------|
185
+ | <code>そうすれば彼女は天吾をほめてくれた。</code> | <code>За це вона його хвалила.</code> |
186
+ | <code>「警察官一家」</code> | <code>— Поліцейська родина.</code> |
187
+ | <code>ある、とバーテンダーは言った。</code> | <code>Бармен відповів, що є.</code> |
188
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
189
+ ```json
190
+ {
191
+ "scale": 20.0,
192
+ "similarity_fct": "cos_sim"
193
+ }
194
+ ```
195
+
196
+ ### Training Hyperparameters
197
+ #### Non-Default Hyperparameters
198
+
199
+ - `eval_strategy`: steps
200
+ - `per_device_train_batch_size`: 16
201
+ - `per_device_eval_batch_size`: 16
202
+ - `num_train_epochs`: 4
203
+ - `warmup_ratio`: 0.1
204
+ - `fp16`: True
205
+ - `batch_sampler`: no_duplicates
206
+
207
+ #### All Hyperparameters
208
+ <details><summary>Click to expand</summary>
209
+
210
+ - `overwrite_output_dir`: False
211
+ - `do_predict`: False
212
+ - `eval_strategy`: steps
213
+ - `prediction_loss_only`: True
214
+ - `per_device_train_batch_size`: 16
215
+ - `per_device_eval_batch_size`: 16
216
+ - `per_gpu_train_batch_size`: None
217
+ - `per_gpu_eval_batch_size`: None
218
+ - `gradient_accumulation_steps`: 1
219
+ - `eval_accumulation_steps`: None
220
+ - `torch_empty_cache_steps`: None
221
+ - `learning_rate`: 5e-05
222
+ - `weight_decay`: 0.0
223
+ - `adam_beta1`: 0.9
224
+ - `adam_beta2`: 0.999
225
+ - `adam_epsilon`: 1e-08
226
+ - `max_grad_norm`: 1.0
227
+ - `num_train_epochs`: 4
228
+ - `max_steps`: -1
229
+ - `lr_scheduler_type`: linear
230
+ - `lr_scheduler_kwargs`: {}
231
+ - `warmup_ratio`: 0.1
232
+ - `warmup_steps`: 0
233
+ - `log_level`: passive
234
+ - `log_level_replica`: warning
235
+ - `log_on_each_node`: True
236
+ - `logging_nan_inf_filter`: True
237
+ - `save_safetensors`: True
238
+ - `save_on_each_node`: False
239
+ - `save_only_model`: False
240
+ - `restore_callback_states_from_checkpoint`: False
241
+ - `no_cuda`: False
242
+ - `use_cpu`: False
243
+ - `use_mps_device`: False
244
+ - `seed`: 42
245
+ - `data_seed`: None
246
+ - `jit_mode_eval`: False
247
+ - `use_ipex`: False
248
+ - `bf16`: False
249
+ - `fp16`: True
250
+ - `fp16_opt_level`: O1
251
+ - `half_precision_backend`: auto
252
+ - `bf16_full_eval`: False
253
+ - `fp16_full_eval`: False
254
+ - `tf32`: None
255
+ - `local_rank`: 0
256
+ - `ddp_backend`: None
257
+ - `tpu_num_cores`: None
258
+ - `tpu_metrics_debug`: False
259
+ - `debug`: []
260
+ - `dataloader_drop_last`: False
261
+ - `dataloader_num_workers`: 0
262
+ - `dataloader_prefetch_factor`: None
263
+ - `past_index`: -1
264
+ - `disable_tqdm`: False
265
+ - `remove_unused_columns`: True
266
+ - `label_names`: None
267
+ - `load_best_model_at_end`: False
268
+ - `ignore_data_skip`: False
269
+ - `fsdp`: []
270
+ - `fsdp_min_num_params`: 0
271
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
272
+ - `fsdp_transformer_layer_cls_to_wrap`: None
273
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
274
+ - `deepspeed`: None
275
+ - `label_smoothing_factor`: 0.0
276
+ - `optim`: adamw_torch
277
+ - `optim_args`: None
278
+ - `adafactor`: False
279
+ - `group_by_length`: False
280
+ - `length_column_name`: length
281
+ - `ddp_find_unused_parameters`: None
282
+ - `ddp_bucket_cap_mb`: None
283
+ - `ddp_broadcast_buffers`: False
284
+ - `dataloader_pin_memory`: True
285
+ - `dataloader_persistent_workers`: False
286
+ - `skip_memory_metrics`: True
287
+ - `use_legacy_prediction_loop`: False
288
+ - `push_to_hub`: False
289
+ - `resume_from_checkpoint`: None
290
+ - `hub_model_id`: None
291
+ - `hub_strategy`: every_save
292
+ - `hub_private_repo`: None
293
+ - `hub_always_push`: False
294
+ - `gradient_checkpointing`: False
295
+ - `gradient_checkpointing_kwargs`: None
296
+ - `include_inputs_for_metrics`: False
297
+ - `include_for_metrics`: []
298
+ - `eval_do_concat_batches`: True
299
+ - `fp16_backend`: auto
300
+ - `push_to_hub_model_id`: None
301
+ - `push_to_hub_organization`: None
302
+ - `mp_parameters`:
303
+ - `auto_find_batch_size`: False
304
+ - `full_determinism`: False
305
+ - `torchdynamo`: None
306
+ - `ray_scope`: last
307
+ - `ddp_timeout`: 1800
308
+ - `torch_compile`: False
309
+ - `torch_compile_backend`: None
310
+ - `torch_compile_mode`: None
311
+ - `dispatch_batches`: None
312
+ - `split_batches`: None
313
+ - `include_tokens_per_second`: False
314
+ - `include_num_input_tokens_seen`: False
315
+ - `neftune_noise_alpha`: None
316
+ - `optim_target_modules`: None
317
+ - `batch_eval_metrics`: False
318
+ - `eval_on_start`: False
319
+ - `use_liger_kernel`: False
320
+ - `eval_use_gather_object`: False
321
+ - `average_tokens_across_devices`: False
322
+ - `prompts`: None
323
+ - `batch_sampler`: no_duplicates
324
+ - `multi_dataset_batch_sampler`: proportional
325
+
326
+ </details>
327
+
328
+ ### Training Logs
329
+ | Epoch | Step | Training Loss | Validation Loss |
330
+ |:------:|:----:|:-------------:|:---------------:|
331
+ | 0.1502 | 100 | 0.0884 | 0.0619 |
332
+ | 0.3003 | 200 | 0.0677 | 0.0591 |
333
+ | 0.4505 | 300 | 0.091 | 0.0778 |
334
+ | 0.6006 | 400 | 0.0612 | 0.0630 |
335
+ | 0.7508 | 500 | 0.0993 | 0.0740 |
336
+ | 0.9009 | 600 | 0.082 | 0.0757 |
337
+ | 1.0511 | 700 | 0.0898 | 0.0722 |
338
+ | 1.2012 | 800 | 0.0342 | 0.0605 |
339
+ | 1.3514 | 900 | 0.0168 | 0.0595 |
340
+ | 1.5015 | 1000 | 0.0158 | 0.0599 |
341
+ | 1.6517 | 1100 | 0.0096 | 0.0613 |
342
+ | 1.8018 | 1200 | 0.0107 | 0.0614 |
343
+ | 1.9520 | 1300 | 0.0113 | 0.0639 |
344
+ | 2.1021 | 1400 | 0.0112 | 0.0572 |
345
+ | 2.2523 | 1500 | 0.0074 | 0.0534 |
346
+ | 2.4024 | 1600 | 0.0039 | 0.0553 |
347
+ | 2.5526 | 1700 | 0.0019 | 0.0532 |
348
+ | 2.7027 | 1800 | 0.0019 | 0.0555 |
349
+ | 2.8529 | 1900 | 0.0026 | 0.0527 |
350
+ | 3.0030 | 2000 | 0.0013 | 0.0525 |
351
+ | 3.1532 | 2100 | 0.0008 | 0.0520 |
352
+ | 3.3033 | 2200 | 0.001 | 0.0516 |
353
+ | 3.4535 | 2300 | 0.0006 | 0.0519 |
354
+ | 3.6036 | 2400 | 0.0006 | 0.0515 |
355
+ | 3.7538 | 2500 | 0.0005 | 0.0514 |
356
+ | 3.9039 | 2600 | 0.0005 | 0.0516 |
357
+
358
+
359
+ ### Framework Versions
360
+ - Python: 3.10.12
361
+ - Sentence Transformers: 3.3.1
362
+ - Transformers: 4.47.1
363
+ - PyTorch: 2.5.1+cu121
364
+ - Accelerate: 1.2.1
365
+ - Datasets: 3.2.0
366
+ - Tokenizers: 0.21.0
367
+
368
+ ## Citation
369
+
370
+ ### BibTeX
371
+
372
+ #### Sentence Transformers
373
+ ```bibtex
374
+ @inproceedings{reimers-2019-sentence-bert,
375
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
376
+ author = "Reimers, Nils and Gurevych, Iryna",
377
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
378
+ month = "11",
379
+ year = "2019",
380
+ publisher = "Association for Computational Linguistics",
381
+ url = "https://arxiv.org/abs/1908.10084",
382
+ }
383
+ ```
384
+
385
+ #### MultipleNegativesRankingLoss
386
+ ```bibtex
387
+ @misc{henderson2017efficient,
388
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
389
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
390
+ year={2017},
391
+ eprint={1705.00652},
392
+ archivePrefix={arXiv},
393
+ primaryClass={cs.CL}
394
+ }
395
+ ```
396
+
397
+ <!--
398
+ ## Glossary
399
+
400
+ *Clearly define terms in order to be accessible across audiences.*
401
+ -->
402
+
403
+ <!--
404
+ ## Model Card Authors
405
+
406
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
407
+ -->
408
+
409
+ <!--
410
+ ## Model Card Contact
411
+
412
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
413
+ -->
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/LaBSE",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "directionality": "bidi",
9
+ "gradient_checkpointing": false,
10
+ "hidden_act": "gelu",
11
+ "hidden_dropout_prob": 0.1,
12
+ "hidden_size": 768,
13
+ "initializer_range": 0.02,
14
+ "intermediate_size": 3072,
15
+ "layer_norm_eps": 1e-12,
16
+ "max_position_embeddings": 512,
17
+ "model_type": "bert",
18
+ "num_attention_heads": 12,
19
+ "num_hidden_layers": 12,
20
+ "pad_token_id": 0,
21
+ "pooler_fc_size": 768,
22
+ "pooler_num_attention_heads": 12,
23
+ "pooler_num_fc_layers": 3,
24
+ "pooler_size_per_head": 128,
25
+ "pooler_type": "first_token_transform",
26
+ "position_embedding_type": "absolute",
27
+ "torch_dtype": "float32",
28
+ "transformers_version": "4.47.1",
29
+ "type_vocab_size": 2,
30
+ "use_cache": true,
31
+ "vocab_size": 501153
32
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.47.1",
5
+ "pytorch": "2.5.1+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:42ad18465fa6e3c11aa9f4b04fc480763d9d0900733333a8776ce38835587220
3
+ size 1883730160
modules.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ },
14
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Dense",
18
+ "type": "sentence_transformers.models.Dense"
19
+ },
20
+ {
21
+ "idx": 3,
22
+ "name": "3",
23
+ "path": "3_Normalize",
24
+ "type": "sentence_transformers.models.Normalize"
25
+ }
26
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 256,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": {
3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "mask_token": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "[PAD]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "sep_token": {
24
+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "unk_token": {
31
+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ }
37
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
3
+ size 13632172
tokenizer_config.json ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": false,
48
+ "extra_special_tokens": {},
49
+ "full_tokenizer_file": null,
50
+ "mask_token": "[MASK]",
51
+ "model_max_length": 256,
52
+ "never_split": null,
53
+ "pad_token": "[PAD]",
54
+ "sep_token": "[SEP]",
55
+ "strip_accents": null,
56
+ "tokenize_chinese_chars": true,
57
+ "tokenizer_class": "BertTokenizer",
58
+ "unk_token": "[UNK]"
59
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff