Hugging Face's logo

aleynahukmet
/
all-MiniLM-L6-v2-8-layers

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9014210
loss:MSELoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
aleynahukmet commited on
Commit
f79b715
·
verified ·
1 Parent(s): ebe5917

Add new SentenceTransformer model

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +592 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +64 -0
  11. vocab.txt +0 -0
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 +1,592 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:9014210
8
+ - loss:MSELoss
9
+ base_model: sentence-transformers/all-MiniLM-L6-v2
10
+ widget:
11
+ - source_sentence: At an outdoor event in an Asian-themed area, a crowd congregates
12
+ as one person in a yellow Chinese dragon costume confronts the camera.
13
+ sentences:
14
+ - Boy dressed in blue holds a toy.
15
+ - the animal is running
16
+ - Two young asian men are squatting.
17
+ - source_sentence: A man with a shopping cart is studying the shelves in a supermarket
18
+ aisle.
19
+ sentences:
20
+ - The children are watching TV at home.
21
+ - Three young boys one is holding a camera and another is holding a green toy all
22
+ are wearing t-shirt and smiling.
23
+ - A large group of people are gathered outside of a brick building lit with spotlights.
24
+ - source_sentence: The door is open.
25
+ sentences:
26
+ - There are three men in this picture, two are on motorbikes, one of the men has
27
+ a large piece of furniture on the back of his bike, the other is about to be handed
28
+ a piece of paper by a man in a white shirt.
29
+ - People are playing music.
30
+ - A girl is using an apple laptop with her headphones in her ears.
31
+ - source_sentence: A small group of children are standing in a classroom and one of
32
+ them has a foot in a trashcan, which also has a rope leading out of it.
33
+ sentences:
34
+ - Children are swimming at the beach.
35
+ - Women are celebrating at a bar.
36
+ - Some men with jerseys are in a bar, watching a soccer match.
37
+ - source_sentence: A black dog is drinking next to a brown and white dog that is looking
38
+ at an orange ball in the lake, whilst a horse and rider passes behind.
39
+ sentences:
40
+ - There are two people running around a track in lane three and the one wearing
41
+ a blue shirt with a green thing over the eyes is just barely ahead of the guy
42
+ wearing an orange shirt and sunglasses.
43
+ - A girl is sitting
44
+ - the guy is dead
45
+ pipeline_tag: sentence-similarity
46
+ library_name: sentence-transformers
47
+ metrics:
48
+ - pearson_cosine
49
+ - spearman_cosine
50
+ - negative_mse
51
+ model-index:
52
+ - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
53
+ results:
54
+ - task:
55
+ type: semantic-similarity
56
+ name: Semantic Similarity
57
+ dataset:
58
+ name: sts dev
59
+ type: sts-dev
60
+ metrics:
61
+ - type: pearson_cosine
62
+ value: 0.8649118460865306
63
+ name: Pearson Cosine
64
+ - type: spearman_cosine
65
+ value: 0.864870367786895
66
+ name: Spearman Cosine
67
+ - task:
68
+ type: knowledge-distillation
69
+ name: Knowledge Distillation
70
+ dataset:
71
+ name: Unknown
72
+ type: unknown
73
+ metrics:
74
+ - type: negative_mse
75
+ value: -0.024522081366740167
76
+ name: Negative Mse
77
+ - task:
78
+ type: semantic-similarity
79
+ name: Semantic Similarity
80
+ dataset:
81
+ name: sts test
82
+ type: sts-test
83
+ metrics:
84
+ - type: pearson_cosine
85
+ value: 0.8202873084532003
86
+ name: Pearson Cosine
87
+ - type: spearman_cosine
88
+ value: 0.8190218550432983
89
+ name: Spearman Cosine
90
+ ---
91
+
92
+ # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
93
+
94
+ 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.
95
+
96
+ ## Model Details
97
+
98
+ ### Model Description
99
+ - **Model Type:** Sentence Transformer
100
+ - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
101
+ - **Maximum Sequence Length:** 256 tokens
102
+ - **Output Dimensionality:** 384 dimensions
103
+ - **Similarity Function:** Cosine Similarity
104
+ <!-- - **Training Dataset:** Unknown -->
105
+ <!-- - **Language:** Unknown -->
106
+ <!-- - **License:** Unknown -->
107
+
108
+ ### Model Sources
109
+
110
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
111
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
112
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
113
+
114
+ ### Full Model Architecture
115
+
116
+ ```
117
+ SentenceTransformer(
118
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
119
+ (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})
120
+ (2): Normalize()
121
+ )
122
+ ```
123
+
124
+ ## Usage
125
+
126
+ ### Direct Usage (Sentence Transformers)
127
+
128
+ First install the Sentence Transformers library:
129
+
130
+ ```bash
131
+ pip install -U sentence-transformers
132
+ ```
133
+
134
+ Then you can load this model and run inference.
135
+ ```python
136
+ from sentence_transformers import SentenceTransformer
137
+
138
+ # Download from the 🤗 Hub
139
+ model = SentenceTransformer("aleynahukmet/all-MiniLM-L6-v2-8-layers")
140
+ # Run inference
141
+ sentences = [
142
+ 'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
143
+ 'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
144
+ 'the guy is dead',
145
+ ]
146
+ embeddings = model.encode(sentences)
147
+ print(embeddings.shape)
148
+ # [3, 384]
149
+
150
+ # Get the similarity scores for the embeddings
151
+ similarities = model.similarity(embeddings, embeddings)
152
+ print(similarities.shape)
153
+ # [3, 3]
154
+ ```
155
+
156
+ <!--
157
+ ### Direct Usage (Transformers)
158
+
159
+ <details><summary>Click to see the direct usage in Transformers</summary>
160
+
161
+ </details>
162
+ -->
163
+
164
+ <!--
165
+ ### Downstream Usage (Sentence Transformers)
166
+
167
+ You can finetune this model on your own dataset.
168
+
169
+ <details><summary>Click to expand</summary>
170
+
171
+ </details>
172
+ -->
173
+
174
+ <!--
175
+ ### Out-of-Scope Use
176
+
177
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
178
+ -->
179
+
180
+ ## Evaluation
181
+
182
+ ### Metrics
183
+
184
+ #### Semantic Similarity
185
+
186
+ * Datasets: `sts-dev` and `sts-test`
187
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
188
+
189
+ | Metric | sts-dev | sts-test |
190
+ |:--------------------|:-----------|:----------|
191
+ | pearson_cosine | 0.8649 | 0.8203 |
192
+ | **spearman_cosine** | **0.8649** | **0.819** |
193
+
194
+ #### Knowledge Distillation
195
+
196
+ * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
197
+
198
+ | Metric | Value |
199
+ |:-----------------|:------------|
200
+ | **negative_mse** | **-0.0245** |
201
+
202
+ <!--
203
+ ## Bias, Risks and Limitations
204
+
205
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
206
+ -->
207
+
208
+ <!--
209
+ ### Recommendations
210
+
211
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
212
+ -->
213
+
214
+ ## Training Details
215
+
216
+ ### Training Dataset
217
+
218
+ #### Unnamed Dataset
219
+
220
+
221
+ * Size: 9,014,210 training samples
222
+ * Columns: <code>sentence</code> and <code>label</code>
223
+ * Approximate statistics based on the first 1000 samples:
224
+ | | sentence | label |
225
+ |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
226
+ | type | string | list |
227
+ | details | <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
228
+ * Samples:
229
+ | sentence | label |
230
+ |:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
231
+ | <code>A person on a horse jumps over a broken down airplane.</code> | <code>[-0.009216307662427425, 0.003964003175497055, 0.04029734805226326, 0.0030935262329876423, -0.03516044840216637, ...]</code> |
232
+ | <code>Children smiling and waving at camera</code> | <code>[-0.03215238079428673, 0.06086821109056473, 0.013251038268208504, -0.017755677923560143, 0.07927625626325607, ...]</code> |
233
+ | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>[-0.020561737939715385, -0.03641558438539505, -0.039370208978652954, -0.0975518748164177, 0.005307587794959545, ...]</code> |
234
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
235
+
236
+ ### Evaluation Dataset
237
+
238
+ #### Unnamed Dataset
239
+
240
+
241
+ * Size: 10,000 evaluation samples
242
+ * Columns: <code>sentence</code> and <code>label</code>
243
+ * Approximate statistics based on the first 1000 samples:
244
+ | | sentence | label |
245
+ |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
246
+ | type | string | list |
247
+ | details | <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
248
+ * Samples:
249
+ | sentence | label |
250
+ |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
251
+ | <code>Two women are embracing while holding to go packages.</code> | <code>[-0.007923883385956287, -0.024198176339268684, 0.034445445984601974, 0.036053989082574844, -0.06740871071815491, ...]</code> |
252
+ | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>[-0.08869566023349762, 0.02789478376507759, 0.060685668140649796, -0.02580258436501026, 0.008359752595424652, ...]</code> |
253
+ | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>[0.027255145832896233, 0.07622072845697403, 0.025504805147647858, -0.0542026124894619, -0.052822694182395935, ...]</code> |
254
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
255
+
256
+ ### Training Hyperparameters
257
+ #### Non-Default Hyperparameters
258
+
259
+ - `eval_strategy`: steps
260
+ - `per_device_train_batch_size`: 64
261
+ - `per_device_eval_batch_size`: 64
262
+ - `learning_rate`: 0.0001
263
+ - `num_train_epochs`: 1
264
+ - `warmup_ratio`: 0.1
265
+ - `fp16`: True
266
+ - `load_best_model_at_end`: True
267
+
268
+ #### All Hyperparameters
269
+ <details><summary>Click to expand</summary>
270
+
271
+ - `overwrite_output_dir`: False
272
+ - `do_predict`: False
273
+ - `eval_strategy`: steps
274
+ - `prediction_loss_only`: True
275
+ - `per_device_train_batch_size`: 64
276
+ - `per_device_eval_batch_size`: 64
277
+ - `per_gpu_train_batch_size`: None
278
+ - `per_gpu_eval_batch_size`: None
279
+ - `gradient_accumulation_steps`: 1
280
+ - `eval_accumulation_steps`: None
281
+ - `torch_empty_cache_steps`: None
282
+ - `learning_rate`: 0.0001
283
+ - `weight_decay`: 0.0
284
+ - `adam_beta1`: 0.9
285
+ - `adam_beta2`: 0.999
286
+ - `adam_epsilon`: 1e-08
287
+ - `max_grad_norm`: 1.0
288
+ - `num_train_epochs`: 1
289
+ - `max_steps`: -1
290
+ - `lr_scheduler_type`: linear
291
+ - `lr_scheduler_kwargs`: {}
292
+ - `warmup_ratio`: 0.1
293
+ - `warmup_steps`: 0
294
+ - `log_level`: passive
295
+ - `log_level_replica`: warning
296
+ - `log_on_each_node`: True
297
+ - `logging_nan_inf_filter`: True
298
+ - `save_safetensors`: True
299
+ - `save_on_each_node`: False
300
+ - `save_only_model`: False
301
+ - `restore_callback_states_from_checkpoint`: False
302
+ - `no_cuda`: False
303
+ - `use_cpu`: False
304
+ - `use_mps_device`: False
305
+ - `seed`: 42
306
+ - `data_seed`: None
307
+ - `jit_mode_eval`: False
308
+ - `use_ipex`: False
309
+ - `bf16`: False
310
+ - `fp16`: True
311
+ - `fp16_opt_level`: O1
312
+ - `half_precision_backend`: auto
313
+ - `bf16_full_eval`: False
314
+ - `fp16_full_eval`: False
315
+ - `tf32`: None
316
+ - `local_rank`: 0
317
+ - `ddp_backend`: None
318
+ - `tpu_num_cores`: None
319
+ - `tpu_metrics_debug`: False
320
+ - `debug`: []
321
+ - `dataloader_drop_last`: False
322
+ - `dataloader_num_workers`: 0
323
+ - `dataloader_prefetch_factor`: None
324
+ - `past_index`: -1
325
+ - `disable_tqdm`: False
326
+ - `remove_unused_columns`: True
327
+ - `label_names`: None
328
+ - `load_best_model_at_end`: True
329
+ - `ignore_data_skip`: False
330
+ - `fsdp`: []
331
+ - `fsdp_min_num_params`: 0
332
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
333
+ - `fsdp_transformer_layer_cls_to_wrap`: None
334
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
335
+ - `deepspeed`: None
336
+ - `label_smoothing_factor`: 0.0
337
+ - `optim`: adamw_torch
338
+ - `optim_args`: None
339
+ - `adafactor`: False
340
+ - `group_by_length`: False
341
+ - `length_column_name`: length
342
+ - `ddp_find_unused_parameters`: None
343
+ - `ddp_bucket_cap_mb`: None
344
+ - `ddp_broadcast_buffers`: False
345
+ - `dataloader_pin_memory`: True
346
+ - `dataloader_persistent_workers`: False
347
+ - `skip_memory_metrics`: True
348
+ - `use_legacy_prediction_loop`: False
349
+ - `push_to_hub`: False
350
+ - `resume_from_checkpoint`: None
351
+ - `hub_model_id`: None
352
+ - `hub_strategy`: every_save
353
+ - `hub_private_repo`: False
354
+ - `hub_always_push`: False
355
+ - `gradient_checkpointing`: False
356
+ - `gradient_checkpointing_kwargs`: None
357
+ - `include_inputs_for_metrics`: False
358
+ - `eval_do_concat_batches`: True
359
+ - `fp16_backend`: auto
360
+ - `push_to_hub_model_id`: None
361
+ - `push_to_hub_organization`: None
362
+ - `mp_parameters`:
363
+ - `auto_find_batch_size`: False
364
+ - `full_determinism`: False
365
+ - `torchdynamo`: None
366
+ - `ray_scope`: last
367
+ - `ddp_timeout`: 1800
368
+ - `torch_compile`: False
369
+ - `torch_compile_backend`: None
370
+ - `torch_compile_mode`: None
371
+ - `dispatch_batches`: None
372
+ - `split_batches`: None
373
+ - `include_tokens_per_second`: False
374
+ - `include_num_input_tokens_seen`: False
375
+ - `neftune_noise_alpha`: None
376
+ - `optim_target_modules`: None
377
+ - `batch_eval_metrics`: False
378
+ - `eval_on_start`: False
379
+ - `eval_use_gather_object`: False
380
+ - `prompts`: None
381
+ - `batch_sampler`: batch_sampler
382
+ - `multi_dataset_batch_sampler`: proportional
383
+
384
+ </details>
385
+
386
+ ### Training Logs
387
+ <details><summary>Click to expand</summary>
388
+
389
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | negative_mse | sts-test_spearman_cosine |
390
+ |:---------:|:----------:|:-------------:|:---------------:|:-----------------------:|:------------:|:------------------------:|
391
+ | 0 | 0 | - | - | 0.7048 | -0.3846 | - |
392
+ | 0.0071 | 1000 | 0.0032 | - | - | - | - |
393
+ | 0.0142 | 2000 | 0.0023 | - | - | - | - |
394
+ | 0.0213 | 3000 | 0.0019 | - | - | - | - |
395
+ | 0.0284 | 4000 | 0.0017 | - | - | - | - |
396
+ | 0.0355 | 5000 | 0.0015 | 0.0013 | 0.8149 | -0.1309 | - |
397
+ | 0.0426 | 6000 | 0.0014 | - | - | - | - |
398
+ | 0.0497 | 7000 | 0.0012 | - | - | - | - |
399
+ | 0.0568 | 8000 | 0.0011 | - | - | - | - |
400
+ | 0.0639 | 9000 | 0.001 | - | - | - | - |
401
+ | 0.0710 | 10000 | 0.001 | 0.0008 | 0.8495 | -0.0754 | - |
402
+ | 0.0781 | 11000 | 0.0009 | - | - | - | - |
403
+ | 0.0852 | 12000 | 0.0008 | - | - | - | - |
404
+ | 0.0923 | 13000 | 0.0008 | - | - | - | - |
405
+ | 0.0994 | 14000 | 0.0007 | - | - | - | - |
406
+ | 0.1065 | 15000 | 0.0007 | 0.0005 | 0.8569 | -0.0528 | - |
407
+ | 0.1136 | 16000 | 0.0007 | - | - | - | - |
408
+ | 0.1207 | 17000 | 0.0007 | - | - | - | - |
409
+ | 0.1278 | 18000 | 0.0006 | - | - | - | - |
410
+ | 0.1349 | 19000 | 0.0006 | - | - | - | - |
411
+ | 0.1420 | 20000 | 0.0006 | 0.0004 | 0.8589 | -0.0438 | - |
412
+ | 0.1491 | 21000 | 0.0006 | - | - | - | - |
413
+ | 0.1562 | 22000 | 0.0006 | - | - | - | - |
414
+ | 0.1633 | 23000 | 0.0006 | - | - | - | - |
415
+ | 0.1704 | 24000 | 0.0006 | - | - | - | - |
416
+ | 0.1775 | 25000 | 0.0005 | 0.0004 | 0.8608 | -0.0392 | - |
417
+ | 0.1846 | 26000 | 0.0005 | - | - | - | - |
418
+ | 0.1917 | 27000 | 0.0005 | - | - | - | - |
419
+ | 0.1988 | 28000 | 0.0005 | - | - | - | - |
420
+ | 0.2059 | 29000 | 0.0005 | - | - | - | - |
421
+ | 0.2130 | 30000 | 0.0005 | 0.0004 | 0.8619 | -0.0363 | - |
422
+ | 0.2201 | 31000 | 0.0005 | - | - | - | - |
423
+ | 0.2272 | 32000 | 0.0005 | - | - | - | - |
424
+ | 0.2343 | 33000 | 0.0005 | - | - | - | - |
425
+ | 0.2414 | 34000 | 0.0005 | - | - | - | - |
426
+ | 0.2485 | 35000 | 0.0005 | 0.0003 | 0.8619 | -0.0343 | - |
427
+ | 0.2556 | 36000 | 0.0005 | - | - | - | - |
428
+ | 0.2627 | 37000 | 0.0005 | - | - | - | - |
429
+ | 0.2698 | 38000 | 0.0005 | - | - | - | - |
430
+ | 0.2769 | 39000 | 0.0005 | - | - | - | - |
431
+ | 0.2840 | 40000 | 0.0005 | 0.0003 | 0.8613 | -0.0329 | - |
432
+ | 0.2911 | 41000 | 0.0005 | - | - | - | - |
433
+ | 0.2982 | 42000 | 0.0005 | - | - | - | - |
434
+ | 0.3053 | 43000 | 0.0005 | - | - | - | - |
435
+ | 0.3124 | 44000 | 0.0005 | - | - | - | - |
436
+ | 0.3195 | 45000 | 0.0005 | 0.0003 | 0.8633 | -0.0316 | - |
437
+ | 0.3266 | 46000 | 0.0005 | - | - | - | - |
438
+ | 0.3337 | 47000 | 0.0005 | - | - | - | - |
439
+ | 0.3408 | 48000 | 0.0005 | - | - | - | - |
440
+ | 0.3479 | 49000 | 0.0004 | - | - | - | - |
441
+ | 0.3550 | 50000 | 0.0004 | 0.0003 | 0.8631 | -0.0306 | - |
442
+ | 0.3621 | 51000 | 0.0004 | - | - | - | - |
443
+ | 0.3692 | 52000 | 0.0004 | - | - | - | - |
444
+ | 0.3763 | 53000 | 0.0004 | - | - | - | - |
445
+ | 0.3834 | 54000 | 0.0004 | - | - | - | - |
446
+ | 0.3905 | 55000 | 0.0004 | 0.0003 | 0.8635 | -0.0297 | - |
447
+ | 0.3976 | 56000 | 0.0004 | - | - | - | - |
448
+ | 0.4047 | 57000 | 0.0004 | - | - | - | - |
449
+ | 0.4118 | 58000 | 0.0004 | - | - | - | - |
450
+ | 0.4189 | 59000 | 0.0004 | - | - | - | - |
451
+ | 0.4260 | 60000 | 0.0004 | 0.0003 | 0.8640 | -0.0290 | - |
452
+ | 0.4331 | 61000 | 0.0004 | - | - | - | - |
453
+ | 0.4402 | 62000 | 0.0004 | - | - | - | - |
454
+ | 0.4473 | 63000 | 0.0004 | - | - | - | - |
455
+ | 0.4544 | 64000 | 0.0004 | - | - | - | - |
456
+ | 0.4615 | 65000 | 0.0004 | 0.0003 | 0.8644 | -0.0285 | - |
457
+ | 0.4686 | 66000 | 0.0004 | - | - | - | - |
458
+ | 0.4757 | 67000 | 0.0004 | - | - | - | - |
459
+ | 0.4828 | 68000 | 0.0004 | - | - | - | - |
460
+ | 0.4899 | 69000 | 0.0004 | - | - | - | - |
461
+ | 0.4970 | 70000 | 0.0004 | 0.0003 | 0.8641 | -0.0280 | - |
462
+ | 0.5041 | 71000 | 0.0004 | - | - | - | - |
463
+ | 0.5112 | 72000 | 0.0004 | - | - | - | - |
464
+ | 0.5183 | 73000 | 0.0004 | - | - | - | - |
465
+ | 0.5254 | 74000 | 0.0004 | - | - | - | - |
466
+ | 0.5325 | 75000 | 0.0004 | 0.0003 | 0.8648 | -0.0276 | - |
467
+ | 0.5396 | 76000 | 0.0004 | - | - | - | - |
468
+ | 0.5467 | 77000 | 0.0004 | - | - | - | - |
469
+ | 0.5538 | 78000 | 0.0004 | - | - | - | - |
470
+ | 0.5609 | 79000 | 0.0004 | - | - | - | - |
471
+ | 0.5680 | 80000 | 0.0004 | 0.0003 | 0.8644 | -0.0271 | - |
472
+ | 0.5751 | 81000 | 0.0004 | - | - | - | - |
473
+ | 0.5822 | 82000 | 0.0004 | - | - | - | - |
474
+ | 0.5893 | 83000 | 0.0004 | - | - | - | - |
475
+ | 0.5964 | 84000 | 0.0004 | - | - | - | - |
476
+ | 0.6035 | 85000 | 0.0004 | 0.0003 | 0.8648 | -0.0267 | - |
477
+ | 0.6106 | 86000 | 0.0004 | - | - | - | - |
478
+ | 0.6177 | 87000 | 0.0004 | - | - | - | - |
479
+ | 0.6248 | 88000 | 0.0004 | - | - | - | - |
480
+ | 0.6319 | 89000 | 0.0004 | - | - | - | - |
481
+ | 0.6390 | 90000 | 0.0004 | 0.0003 | 0.8645 | -0.0264 | - |
482
+ | 0.6461 | 91000 | 0.0004 | - | - | - | - |
483
+ | 0.6532 | 92000 | 0.0004 | - | - | - | - |
484
+ | 0.6603 | 93000 | 0.0004 | - | - | - | - |
485
+ | 0.6674 | 94000 | 0.0004 | - | - | - | - |
486
+ | 0.6745 | 95000 | 0.0004 | 0.0003 | 0.8643 | -0.0261 | - |
487
+ | 0.6816 | 96000 | 0.0004 | - | - | - | - |
488
+ | 0.6887 | 97000 | 0.0004 | - | - | - | - |
489
+ | 0.6958 | 98000 | 0.0004 | - | - | - | - |
490
+ | 0.7029 | 99000 | 0.0004 | - | - | - | - |
491
+ | 0.7100 | 100000 | 0.0004 | 0.0003 | 0.8643 | -0.0259 | - |
492
+ | 0.7171 | 101000 | 0.0004 | - | - | - | - |
493
+ | 0.7242 | 102000 | 0.0004 | - | - | - | - |
494
+ | 0.7313 | 103000 | 0.0004 | - | - | - | - |
495
+ | 0.7384 | 104000 | 0.0004 | - | - | - | - |
496
+ | 0.7455 | 105000 | 0.0004 | 0.0003 | 0.8646 | -0.0257 | - |
497
+ | 0.7526 | 106000 | 0.0004 | - | - | - | - |
498
+ | 0.7597 | 107000 | 0.0004 | - | - | - | - |
499
+ | 0.7668 | 108000 | 0.0004 | - | - | - | - |
500
+ | 0.7739 | 109000 | 0.0004 | - | - | - | - |
501
+ | 0.7810 | 110000 | 0.0004 | 0.0003 | 0.8637 | -0.0254 | - |
502
+ | 0.7881 | 111000 | 0.0004 | - | - | - | - |
503
+ | 0.7952 | 112000 | 0.0004 | - | - | - | - |
504
+ | 0.8023 | 113000 | 0.0004 | - | - | - | - |
505
+ | 0.8094 | 114000 | 0.0004 | - | - | - | - |
506
+ | 0.8165 | 115000 | 0.0004 | 0.0003 | 0.8643 | -0.0252 | - |
507
+ | 0.8236 | 116000 | 0.0004 | - | - | - | - |
508
+ | 0.8307 | 117000 | 0.0004 | - | - | - | - |
509
+ | 0.8378 | 118000 | 0.0004 | - | - | - | - |
510
+ | 0.8449 | 119000 | 0.0004 | - | - | - | - |
511
+ | 0.8520 | 120000 | 0.0004 | 0.0003 | 0.8645 | -0.0250 | - |
512
+ | 0.8591 | 121000 | 0.0004 | - | - | - | - |
513
+ | 0.8662 | 122000 | 0.0004 | - | - | - | - |
514
+ | 0.8733 | 123000 | 0.0004 | - | - | - | - |
515
+ | 0.8804 | 124000 | 0.0004 | - | - | - | - |
516
+ | 0.8875 | 125000 | 0.0004 | 0.0002 | 0.8646 | -0.0248 | - |
517
+ | 0.8946 | 126000 | 0.0004 | - | - | - | - |
518
+ | 0.9017 | 127000 | 0.0004 | - | - | - | - |
519
+ | 0.9088 | 128000 | 0.0004 | - | - | - | - |
520
+ | 0.9159 | 129000 | 0.0004 | - | - | - | - |
521
+ | 0.9230 | 130000 | 0.0004 | 0.0002 | 0.8647 | -0.0247 | - |
522
+ | 0.9301 | 131000 | 0.0004 | - | - | - | - |
523
+ | 0.9372 | 132000 | 0.0004 | - | - | - | - |
524
+ | 0.9443 | 133000 | 0.0004 | - | - | - | - |
525
+ | 0.9514 | 134000 | 0.0004 | - | - | - | - |
526
+ | 0.9585 | 135000 | 0.0004 | 0.0002 | 0.8646 | -0.0246 | - |
527
+ | 0.9656 | 136000 | 0.0004 | - | - | - | - |
528
+ | 0.9727 | 137000 | 0.0004 | - | - | - | - |
529
+ | 0.9798 | 138000 | 0.0004 | - | - | - | - |
530
+ | 0.9869 | 139000 | 0.0004 | - | - | - | - |
531
+ | **0.994** | **140000** | **0.0004** | **0.0002** | **0.8649** | **-0.0245** | **-** |
532
+ | 1.0 | 140848 | - | - | - | - | 0.8190 |
533
+
534
+ * The bold row denotes the saved checkpoint.
535
+ </details>
536
+
537
+ ### Framework Versions
538
+ - Python: 3.12.4
539
+ - Sentence Transformers: 3.3.1
540
+ - Transformers: 4.44.2
541
+ - PyTorch: 2.4.1+cu121
542
+ - Accelerate: 1.0.1
543
+ - Datasets: 2.19.0
544
+ - Tokenizers: 0.19.1
545
+
546
+ ## Citation
547
+
548
+ ### BibTeX
549
+
550
+ #### Sentence Transformers
551
+ ```bibtex
552
+ @inproceedings{reimers-2019-sentence-bert,
553
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
554
+ author = "Reimers, Nils and Gurevych, Iryna",
555
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
556
+ month = "11",
557
+ year = "2019",
558
+ publisher = "Association for Computational Linguistics",
559
+ url = "https://arxiv.org/abs/1908.10084",
560
+ }
561
+ ```
562
+
563
+ #### MSELoss
564
+ ```bibtex
565
+ @inproceedings{reimers-2020-multilingual-sentence-bert,
566
+ title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
567
+ author = "Reimers, Nils and Gurevych, Iryna",
568
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
569
+ month = "11",
570
+ year = "2020",
571
+ publisher = "Association for Computational Linguistics",
572
+ url = "https://arxiv.org/abs/2004.09813",
573
+ }
574
+ ```
575
+
576
+ <!--
577
+ ## Glossary
578
+
579
+ *Clearly define terms in order to be accessible across audiences.*
580
+ -->
581
+
582
+ <!--
583
+ ## Model Card Authors
584
+
585
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
586
+ -->
587
+
588
+ <!--
589
+ ## Model Card Contact
590
+
591
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
592
+ -->
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": 8,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.44.2",
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": "3.3.1",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.4.1+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 sha256:7f907b1b6b586b6cee3d82ee12c90b646e1a44b8319ec615f2cd8d5ef9643c7f
3
+ size 62465680
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