LeoChiuu commited on
Commit
7a9cf71
1 Parent(s): 45e24aa

Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 384,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
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
+ }
README.md ADDED
@@ -0,0 +1,405 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: []
3
+ library_name: sentence-transformers
4
+ tags:
5
+ - sentence-transformers
6
+ - sentence-similarity
7
+ - feature-extraction
8
+ - generated_from_trainer
9
+ - dataset_size:38688
10
+ - loss:ContrastiveLoss
11
+ base_model: sentence-transformers/all-MiniLM-L6-v2
12
+ datasets: []
13
+ widget:
14
+ - source_sentence: There is a heavy cost for this service provided in conjunction
15
+ with NOAA and SARSAT.
16
+ sentences:
17
+ - No significant changes have been made to the roadway except for its legal definition.
18
+ - Some academics have questioned the ethics of these payments.
19
+ - There is no charge for this service provided in conjunction with NOAA and SARSAT.
20
+ - source_sentence: You're not thin.
21
+ sentences:
22
+ - This process is called low-dimensional embedded in machine learning.
23
+ - You're thin.
24
+ - Jean Prouvost was the founder of Marie Claire.
25
+ - source_sentence: The lead man is charisma-free.
26
+ sentences:
27
+ - Fossil egg s are rare, but one oogenus, Polyclonoolithus, was discovered in the
28
+ Hekou Group.
29
+ - The roof is shingled, and topped by a small belfry.
30
+ - The lead man doesn't have charisma.
31
+ - source_sentence: Willis has criticized the rules adopted by the RNC, particularly
32
+ Rules 12, 16, and 40.
33
+ sentences:
34
+ - Willis has fully accepted the rules adopted by the RNC, particularly Rules 12,
35
+ 16, and 40.
36
+ - I can't stop reading.
37
+ - This force acts on water independently of the wind stress.
38
+ - source_sentence: The publication was named after Sir James Joynton Smith.
39
+ sentences:
40
+ - Detailed specific information on the ongoing validation activities is being made
41
+ available in related publications.
42
+ - On November 25, 2012, Tom O'Brien was reinstated.
43
+ - The publication took its name from its founder and chief financer Sir James Joynton
44
+ Smith.
45
+ pipeline_tag: sentence-similarity
46
+ ---
47
+
48
+ # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
49
+
50
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
51
+
52
+ ## Model Details
53
+
54
+ ### Model Description
55
+ - **Model Type:** Sentence Transformer
56
+ - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
57
+ - **Maximum Sequence Length:** 256 tokens
58
+ - **Output Dimensionality:** 384 tokens
59
+ - **Similarity Function:** Cosine Similarity
60
+ <!-- - **Training Dataset:** Unknown -->
61
+ <!-- - **Language:** Unknown -->
62
+ <!-- - **License:** Unknown -->
63
+
64
+ ### Model Sources
65
+
66
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
67
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
68
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
69
+
70
+ ### Full Model Architecture
71
+
72
+ ```
73
+ SentenceTransformer(
74
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
75
+ (1): Pooling({'word_embedding_dimension': 384, '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})
76
+ (2): Normalize()
77
+ )
78
+ ```
79
+
80
+ ## Usage
81
+
82
+ ### Direct Usage (Sentence Transformers)
83
+
84
+ First install the Sentence Transformers library:
85
+
86
+ ```bash
87
+ pip install -U sentence-transformers
88
+ ```
89
+
90
+ Then you can load this model and run inference.
91
+ ```python
92
+ from sentence_transformers import SentenceTransformer
93
+
94
+ # Download from the 🤗 Hub
95
+ model = SentenceTransformer("LeoChiuu/all-MiniLM-L6-v2-negations")
96
+ # Run inference
97
+ sentences = [
98
+ 'The publication was named after Sir James Joynton Smith.',
99
+ 'The publication took its name from its founder and chief financer Sir James Joynton Smith.',
100
+ "On November 25, 2012, Tom O'Brien was reinstated.",
101
+ ]
102
+ embeddings = model.encode(sentences)
103
+ print(embeddings.shape)
104
+ # [3, 384]
105
+
106
+ # Get the similarity scores for the embeddings
107
+ similarities = model.similarity(embeddings, embeddings)
108
+ print(similarities.shape)
109
+ # [3, 3]
110
+ ```
111
+
112
+ <!--
113
+ ### Direct Usage (Transformers)
114
+
115
+ <details><summary>Click to see the direct usage in Transformers</summary>
116
+
117
+ </details>
118
+ -->
119
+
120
+ <!--
121
+ ### Downstream Usage (Sentence Transformers)
122
+
123
+ You can finetune this model on your own dataset.
124
+
125
+ <details><summary>Click to expand</summary>
126
+
127
+ </details>
128
+ -->
129
+
130
+ <!--
131
+ ### Out-of-Scope Use
132
+
133
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
134
+ -->
135
+
136
+ <!--
137
+ ## Bias, Risks and Limitations
138
+
139
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
140
+ -->
141
+
142
+ <!--
143
+ ### Recommendations
144
+
145
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
146
+ -->
147
+
148
+ ## Training Details
149
+
150
+ ### Training Dataset
151
+
152
+ #### Unnamed Dataset
153
+
154
+
155
+ * Size: 38,688 training samples
156
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
157
+ * Approximate statistics based on the first 1000 samples:
158
+ | | sentence_0 | sentence_1 | label |
159
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
160
+ | type | string | string | int |
161
+ | details | <ul><li>min: 5 tokens</li><li>mean: 15.94 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.96 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>0: ~48.50%</li><li>1: ~51.50%</li></ul> |
162
+ * Samples:
163
+ | sentence_0 | sentence_1 | label |
164
+ |:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
165
+ | <code>No, that is impossible.</code> | <code>No, that is not possible.</code> | <code>0</code> |
166
+ | <code>The building did indeed serve as a hof, according to the bone finds.</code> | <code>The bone finds thus indicate the building did indeed serve as a hof.</code> | <code>0</code> |
167
+ | <code>The building became a pet shop.</code> | <code>The building became a hospital.</code> | <code>1</code> |
168
+ * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
169
+ ```json
170
+ {
171
+ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
172
+ "margin": 0.5,
173
+ "size_average": true
174
+ }
175
+ ```
176
+
177
+ ### Training Hyperparameters
178
+ #### Non-Default Hyperparameters
179
+
180
+ - `per_device_train_batch_size`: 16
181
+ - `per_device_eval_batch_size`: 16
182
+ - `num_train_epochs`: 10
183
+ - `multi_dataset_batch_sampler`: round_robin
184
+
185
+ #### All Hyperparameters
186
+ <details><summary>Click to expand</summary>
187
+
188
+ - `overwrite_output_dir`: False
189
+ - `do_predict`: False
190
+ - `prediction_loss_only`: True
191
+ - `per_device_train_batch_size`: 16
192
+ - `per_device_eval_batch_size`: 16
193
+ - `per_gpu_train_batch_size`: None
194
+ - `per_gpu_eval_batch_size`: None
195
+ - `gradient_accumulation_steps`: 1
196
+ - `eval_accumulation_steps`: None
197
+ - `learning_rate`: 5e-05
198
+ - `weight_decay`: 0.0
199
+ - `adam_beta1`: 0.9
200
+ - `adam_beta2`: 0.999
201
+ - `adam_epsilon`: 1e-08
202
+ - `max_grad_norm`: 1
203
+ - `num_train_epochs`: 10
204
+ - `max_steps`: -1
205
+ - `lr_scheduler_type`: linear
206
+ - `lr_scheduler_kwargs`: {}
207
+ - `warmup_ratio`: 0.0
208
+ - `warmup_steps`: 0
209
+ - `log_level`: passive
210
+ - `log_level_replica`: warning
211
+ - `log_on_each_node`: True
212
+ - `logging_nan_inf_filter`: True
213
+ - `save_safetensors`: True
214
+ - `save_on_each_node`: False
215
+ - `save_only_model`: False
216
+ - `no_cuda`: False
217
+ - `use_cpu`: False
218
+ - `use_mps_device`: False
219
+ - `seed`: 42
220
+ - `data_seed`: None
221
+ - `jit_mode_eval`: False
222
+ - `use_ipex`: False
223
+ - `bf16`: False
224
+ - `fp16`: False
225
+ - `fp16_opt_level`: O1
226
+ - `half_precision_backend`: auto
227
+ - `bf16_full_eval`: False
228
+ - `fp16_full_eval`: False
229
+ - `tf32`: None
230
+ - `local_rank`: 0
231
+ - `ddp_backend`: None
232
+ - `tpu_num_cores`: None
233
+ - `tpu_metrics_debug`: False
234
+ - `debug`: []
235
+ - `dataloader_drop_last`: False
236
+ - `dataloader_num_workers`: 0
237
+ - `dataloader_prefetch_factor`: None
238
+ - `past_index`: -1
239
+ - `disable_tqdm`: False
240
+ - `remove_unused_columns`: True
241
+ - `label_names`: None
242
+ - `load_best_model_at_end`: False
243
+ - `ignore_data_skip`: False
244
+ - `fsdp`: []
245
+ - `fsdp_min_num_params`: 0
246
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
247
+ - `fsdp_transformer_layer_cls_to_wrap`: None
248
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
249
+ - `deepspeed`: None
250
+ - `label_smoothing_factor`: 0.0
251
+ - `optim`: adamw_torch
252
+ - `optim_args`: None
253
+ - `adafactor`: False
254
+ - `group_by_length`: False
255
+ - `length_column_name`: length
256
+ - `ddp_find_unused_parameters`: None
257
+ - `ddp_bucket_cap_mb`: None
258
+ - `ddp_broadcast_buffers`: False
259
+ - `dataloader_pin_memory`: True
260
+ - `dataloader_persistent_workers`: False
261
+ - `skip_memory_metrics`: True
262
+ - `use_legacy_prediction_loop`: False
263
+ - `push_to_hub`: False
264
+ - `resume_from_checkpoint`: None
265
+ - `hub_model_id`: None
266
+ - `hub_strategy`: every_save
267
+ - `hub_private_repo`: False
268
+ - `hub_always_push`: False
269
+ - `gradient_checkpointing`: False
270
+ - `gradient_checkpointing_kwargs`: None
271
+ - `include_inputs_for_metrics`: False
272
+ - `eval_do_concat_batches`: True
273
+ - `fp16_backend`: auto
274
+ - `push_to_hub_model_id`: None
275
+ - `push_to_hub_organization`: None
276
+ - `mp_parameters`:
277
+ - `auto_find_batch_size`: False
278
+ - `full_determinism`: False
279
+ - `torchdynamo`: None
280
+ - `ray_scope`: last
281
+ - `ddp_timeout`: 1800
282
+ - `torch_compile`: False
283
+ - `torch_compile_backend`: None
284
+ - `torch_compile_mode`: None
285
+ - `dispatch_batches`: None
286
+ - `split_batches`: None
287
+ - `include_tokens_per_second`: False
288
+ - `include_num_input_tokens_seen`: False
289
+ - `neftune_noise_alpha`: None
290
+ - `optim_target_modules`: None
291
+ - `batch_sampler`: batch_sampler
292
+ - `multi_dataset_batch_sampler`: round_robin
293
+
294
+ </details>
295
+
296
+ ### Training Logs
297
+ | Epoch | Step | Training Loss |
298
+ |:------:|:-----:|:-------------:|
299
+ | 0.2068 | 500 | 0.0353 |
300
+ | 0.4136 | 1000 | 0.0307 |
301
+ | 0.6203 | 1500 | 0.0234 |
302
+ | 0.8271 | 2000 | 0.0187 |
303
+ | 1.0339 | 2500 | 0.0152 |
304
+ | 1.2407 | 3000 | 0.0134 |
305
+ | 1.4475 | 3500 | 0.0123 |
306
+ | 1.6543 | 4000 | 0.0111 |
307
+ | 1.8610 | 4500 | 0.0107 |
308
+ | 2.0678 | 5000 | 0.0097 |
309
+ | 2.2746 | 5500 | 0.0096 |
310
+ | 2.4814 | 6000 | 0.0091 |
311
+ | 2.6882 | 6500 | 0.0087 |
312
+ | 2.8950 | 7000 | 0.0086 |
313
+ | 3.1017 | 7500 | 0.0075 |
314
+ | 3.3085 | 8000 | 0.008 |
315
+ | 3.5153 | 8500 | 0.0074 |
316
+ | 3.7221 | 9000 | 0.007 |
317
+ | 3.9289 | 9500 | 0.007 |
318
+ | 4.1356 | 10000 | 0.0063 |
319
+ | 4.3424 | 10500 | 0.0068 |
320
+ | 4.5492 | 11000 | 0.0061 |
321
+ | 4.7560 | 11500 | 0.0059 |
322
+ | 4.9628 | 12000 | 0.0056 |
323
+ | 5.1696 | 12500 | 0.0052 |
324
+ | 5.3763 | 13000 | 0.0055 |
325
+ | 5.5831 | 13500 | 0.0051 |
326
+ | 5.7899 | 14000 | 0.005 |
327
+ | 5.9967 | 14500 | 0.0047 |
328
+ | 6.2035 | 15000 | 0.0046 |
329
+ | 6.4103 | 15500 | 0.0047 |
330
+ | 6.6170 | 16000 | 0.0044 |
331
+ | 6.8238 | 16500 | 0.0044 |
332
+ | 7.0306 | 17000 | 0.0041 |
333
+ | 7.2374 | 17500 | 0.004 |
334
+ | 7.4442 | 18000 | 0.0044 |
335
+ | 7.6510 | 18500 | 0.0039 |
336
+ | 7.8577 | 19000 | 0.0038 |
337
+ | 8.0645 | 19500 | 0.0038 |
338
+ | 8.2713 | 20000 | 0.0037 |
339
+ | 8.4781 | 20500 | 0.0039 |
340
+ | 8.6849 | 21000 | 0.0037 |
341
+ | 8.8916 | 21500 | 0.0036 |
342
+ | 9.0984 | 22000 | 0.0034 |
343
+ | 9.3052 | 22500 | 0.0036 |
344
+ | 9.5120 | 23000 | 0.0035 |
345
+ | 9.7188 | 23500 | 0.0034 |
346
+ | 9.9256 | 24000 | 0.0035 |
347
+
348
+
349
+ ### Framework Versions
350
+ - Python: 3.11.9
351
+ - Sentence Transformers: 3.0.1
352
+ - Transformers: 4.40.2
353
+ - PyTorch: 2.3.0+cpu
354
+ - Accelerate: 0.32.1
355
+ - Datasets: 2.19.1
356
+ - Tokenizers: 0.19.1
357
+
358
+ ## Citation
359
+
360
+ ### BibTeX
361
+
362
+ #### Sentence Transformers
363
+ ```bibtex
364
+ @inproceedings{reimers-2019-sentence-bert,
365
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
366
+ author = "Reimers, Nils and Gurevych, Iryna",
367
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
368
+ month = "11",
369
+ year = "2019",
370
+ publisher = "Association for Computational Linguistics",
371
+ url = "https://arxiv.org/abs/1908.10084",
372
+ }
373
+ ```
374
+
375
+ #### ContrastiveLoss
376
+ ```bibtex
377
+ @inproceedings{hadsell2006dimensionality,
378
+ author={Hadsell, R. and Chopra, S. and LeCun, Y.},
379
+ booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
380
+ title={Dimensionality Reduction by Learning an Invariant Mapping},
381
+ year={2006},
382
+ volume={2},
383
+ number={},
384
+ pages={1735-1742},
385
+ doi={10.1109/CVPR.2006.100}
386
+ }
387
+ ```
388
+
389
+ <!--
390
+ ## Glossary
391
+
392
+ *Clearly define terms in order to be accessible across audiences.*
393
+ -->
394
+
395
+ <!--
396
+ ## Model Card Authors
397
+
398
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
399
+ -->
400
+
401
+ <!--
402
+ ## Model Card Contact
403
+
404
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
405
+ -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 384,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 1536,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 6,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.40.2",
23
+ "type_vocab_size": 2,
24
+ "use_cache": true,
25
+ "vocab_size": 30522
26
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.0.1",
4
+ "transformers": "4.40.2",
5
+ "pytorch": "2.3.0+cpu"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6b68ced173371fb910f0fa0d901c4c1cf752167e673dd1c7a014d80b80d7410a
3
+ size 90864192
modules.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
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
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "mask_token": "[MASK]",
49
+ "max_length": 128,
50
+ "model_max_length": 256,
51
+ "never_split": null,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "[PAD]",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "[SEP]",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff