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kperkins411
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msmarco-distilbert-base-v2_triplet_legal

Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
Generated from Trainer
dataset_size:88018
loss:TripletLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
kperkins411 commited on
Commit
1567d8d
•
1 Parent(s): 9d55b1a

Add new SentenceTransformer model.

Browse files
Files changed (4) hide show
  1. README.md +88 -89
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. tokenizer_config.json +7 -0
README.md CHANGED
@@ -1,5 +1,4 @@
1
  ---
2
- base_model: sentence-transformers/msmarco-distilbert-base-v2
3
  datasets: []
4
  language: []
5
  library_name: sentence-transformers
@@ -188,7 +187,7 @@ widget:
188
  and/or any of its affiliates and the directors, officers and employees of Domini
189
  and/or any of its affiliates.
190
  model-index:
191
- - name: SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-v2
192
  results:
193
  - task:
194
  type: information-retrieval
@@ -198,106 +197,106 @@ model-index:
198
  type: msmarco-distilbert-base-v2
199
  metrics:
200
  - type: cosine_accuracy@1
201
- value: 0.6422082459818309
202
  name: Cosine Accuracy@1
203
  - type: cosine_accuracy@3
204
- value: 0.8230607966457023
205
  name: Cosine Accuracy@3
206
  - type: cosine_accuracy@5
207
- value: 0.872816212438854
208
  name: Cosine Accuracy@5
209
  - type: cosine_accuracy@10
210
- value: 0.9382250174703005
211
  name: Cosine Accuracy@10
212
  - type: cosine_precision@1
213
- value: 0.6422082459818309
214
  name: Cosine Precision@1
215
  - type: cosine_precision@3
216
- value: 0.27435359888190075
217
  name: Cosine Precision@3
218
  - type: cosine_precision@5
219
- value: 0.1745632424877708
220
  name: Cosine Precision@5
221
  - type: cosine_precision@10
222
- value: 0.09382250174703004
223
  name: Cosine Precision@10
224
  - type: cosine_recall@1
225
- value: 0.6422082459818309
226
  name: Cosine Recall@1
227
  - type: cosine_recall@3
228
- value: 0.8230607966457023
229
  name: Cosine Recall@3
230
  - type: cosine_recall@5
231
- value: 0.872816212438854
232
  name: Cosine Recall@5
233
  - type: cosine_recall@10
234
- value: 0.9382250174703005
235
  name: Cosine Recall@10
236
  - type: cosine_ndcg@10
237
- value: 0.790195916846684
238
  name: Cosine Ndcg@10
239
  - type: cosine_mrr@10
240
- value: 0.7427224274289222
241
  name: Cosine Mrr@10
242
  - type: cosine_map@100
243
- value: 0.7454587747656682
244
  name: Cosine Map@100
245
  - type: dot_accuracy@1
246
- value: 0.6317260656883298
247
  name: Dot Accuracy@1
248
  - type: dot_accuracy@3
249
- value: 0.8204053109713487
250
  name: Dot Accuracy@3
251
  - type: dot_accuracy@5
252
- value: 0.8735150244584207
253
  name: Dot Accuracy@5
254
  - type: dot_accuracy@10
255
- value: 0.9375262054507337
256
  name: Dot Accuracy@10
257
  - type: dot_precision@1
258
- value: 0.6317260656883298
259
  name: Dot Precision@1
260
  - type: dot_precision@3
261
- value: 0.27346843699044954
262
  name: Dot Precision@3
263
  - type: dot_precision@5
264
- value: 0.17470300489168414
265
  name: Dot Precision@5
266
  - type: dot_precision@10
267
- value: 0.09375262054507337
268
  name: Dot Precision@10
269
  - type: dot_recall@1
270
- value: 0.6317260656883298
271
  name: Dot Recall@1
272
  - type: dot_recall@3
273
- value: 0.8204053109713487
274
  name: Dot Recall@3
275
  - type: dot_recall@5
276
- value: 0.8735150244584207
277
  name: Dot Recall@5
278
  - type: dot_recall@10
279
- value: 0.9375262054507337
280
  name: Dot Recall@10
281
  - type: dot_ndcg@10
282
- value: 0.7853441093620476
283
  name: Dot Ndcg@10
284
  - type: dot_mrr@10
285
- value: 0.7364890242143864
286
  name: Dot Mrr@10
287
  - type: dot_map@100
288
- value: 0.7392413927907737
289
  name: Dot Map@100
290
  ---
291
 
292
- # SentenceTransformer based on sentence-transformers/msmarco-distilbert-base-v2
293
 
294
- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/msmarco-distilbert-base-v2](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v2). 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.
295
 
296
  ## Model Details
297
 
298
  ### Model Description
299
  - **Model Type:** Sentence Transformer
300
- - **Base model:** [sentence-transformers/msmarco-distilbert-base-v2](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-v2) <!-- at revision 741fcf2d6eabaf0927bfe49c6d9c577df95d3c40 -->
301
  - **Maximum Sequence Length:** 350 tokens
302
  - **Output Dimensionality:** 768 tokens
303
  - **Similarity Function:** Cosine Similarity
@@ -386,36 +385,36 @@ You can finetune this model on your own dataset.
386
 
387
  | Metric | Value |
388
  |:--------------------|:-----------|
389
- | cosine_accuracy@1 | 0.6422 |
390
- | cosine_accuracy@3 | 0.8231 |
391
- | cosine_accuracy@5 | 0.8728 |
392
- | cosine_accuracy@10 | 0.9382 |
393
- | cosine_precision@1 | 0.6422 |
394
- | cosine_precision@3 | 0.2744 |
395
- | cosine_precision@5 | 0.1746 |
396
- | cosine_precision@10 | 0.0938 |
397
- | cosine_recall@1 | 0.6422 |
398
- | cosine_recall@3 | 0.8231 |
399
- | cosine_recall@5 | 0.8728 |
400
- | cosine_recall@10 | 0.9382 |
401
- | cosine_ndcg@10 | 0.7902 |
402
- | cosine_mrr@10 | 0.7427 |
403
- | **cosine_map@100** | **0.7455** |
404
- | dot_accuracy@1 | 0.6317 |
405
- | dot_accuracy@3 | 0.8204 |
406
- | dot_accuracy@5 | 0.8735 |
407
- | dot_accuracy@10 | 0.9375 |
408
- | dot_precision@1 | 0.6317 |
409
- | dot_precision@3 | 0.2735 |
410
- | dot_precision@5 | 0.1747 |
411
- | dot_precision@10 | 0.0938 |
412
- | dot_recall@1 | 0.6317 |
413
- | dot_recall@3 | 0.8204 |
414
- | dot_recall@5 | 0.8735 |
415
- | dot_recall@10 | 0.9375 |
416
- | dot_ndcg@10 | 0.7853 |
417
- | dot_mrr@10 | 0.7365 |
418
- | dot_map@100 | 0.7392 |
419
 
420
  <!--
421
  ## Bias, Risks and Limitations
@@ -612,30 +611,30 @@ You can finetune this model on your own dataset.
612
  ### Training Logs
613
  | Epoch | Step | Training Loss | loss | msmarco-distilbert-base-v2_cosine_map@100 |
614
  |:----------:|:--------:|:-------------:|:----------:|:-----------------------------------------:|
615
- | 0 | 0 | - | - | 0.6601 |
616
- | 0.1453 | 100 | 1.5696 | - | - |
617
- | 0.2907 | 200 | 0.7941 | - | - |
618
- | 0.4360 | 300 | 0.6151 | - | - |
619
- | 0.5814 | 400 | 0.5458 | - | - |
620
- | 0.7267 | 500 | 0.5085 | - | - |
621
- | 0.8721 | 600 | 0.4601 | - | - |
622
- | 1.0131 | 697 | - | 0.3492 | - |
623
- | 1.0044 | 700 | 0.4055 | - | - |
624
- | 1.1497 | 800 | 0.3538 | - | - |
625
- | 1.2951 | 900 | 0.2245 | - | - |
626
- | 1.4404 | 1000 | 0.1821 | - | - |
627
- | 1.5858 | 1100 | 0.1761 | - | - |
628
- | 1.7311 | 1200 | 0.1872 | - | - |
629
- | 1.8765 | 1300 | 0.169 | - | - |
630
- | 2.0131 | 1394 | - | 0.2674 | - |
631
- | 2.0087 | 1400 | 0.1502 | - | - |
632
- | 2.1541 | 1500 | 0.1416 | - | - |
633
- | 2.2994 | 1600 | 0.0914 | - | - |
634
- | 2.4448 | 1700 | 0.0868 | - | - |
635
- | 2.5901 | 1800 | 0.0854 | - | - |
636
- | 2.7355 | 1900 | 0.0905 | - | - |
637
- | 2.8808 | 2000 | 0.0888 | - | - |
638
- | **2.9738** | **2064** | **-** | **0.2272** | **0.7455** |
639
 
640
  * The bold row denotes the saved checkpoint.
641
 
 
1
  ---
 
2
  datasets: []
3
  language: []
4
  library_name: sentence-transformers
 
187
  and/or any of its affiliates and the directors, officers and employees of Domini
188
  and/or any of its affiliates.
189
  model-index:
190
+ - name: SentenceTransformer
191
  results:
192
  - task:
193
  type: information-retrieval
 
197
  type: msmarco-distilbert-base-v2
198
  metrics:
199
  - type: cosine_accuracy@1
200
+ value: 0.3953048087845513
201
  name: Cosine Accuracy@1
202
  - type: cosine_accuracy@3
203
+ value: 0.5342673229837183
204
  name: Cosine Accuracy@3
205
  - type: cosine_accuracy@5
206
+ value: 0.5914426353653919
207
  name: Cosine Accuracy@5
208
  - type: cosine_accuracy@10
209
+ value: 0.66565694812571
210
  name: Cosine Accuracy@10
211
  - type: cosine_precision@1
212
+ value: 0.3953048087845513
213
  name: Cosine Precision@1
214
  - type: cosine_precision@3
215
+ value: 0.17808910766123942
216
  name: Cosine Precision@3
217
  - type: cosine_precision@5
218
+ value: 0.11828852707307837
219
  name: Cosine Precision@5
220
  - type: cosine_precision@10
221
+ value: 0.06656569481257099
222
  name: Cosine Precision@10
223
  - type: cosine_recall@1
224
+ value: 0.3953048087845513
225
  name: Cosine Recall@1
226
  - type: cosine_recall@3
227
+ value: 0.5342673229837183
228
  name: Cosine Recall@3
229
  - type: cosine_recall@5
230
+ value: 0.5914426353653919
231
  name: Cosine Recall@5
232
  - type: cosine_recall@10
233
+ value: 0.66565694812571
234
  name: Cosine Recall@10
235
  - type: cosine_ndcg@10
236
+ value: 0.5240873176000084
237
  name: Cosine Ndcg@10
238
  - type: cosine_mrr@10
239
+ value: 0.4794995582481382
240
  name: Cosine Mrr@10
241
  - type: cosine_map@100
242
+ value: 0.4872380542829767
243
  name: Cosine Map@100
244
  - type: dot_accuracy@1
245
+ value: 0.3934115865202575
246
  name: Dot Accuracy@1
247
  - type: dot_accuracy@3
248
+ value: 0.5312381673608482
249
  name: Dot Accuracy@3
250
  - type: dot_accuracy@5
251
+ value: 0.5899280575539568
252
  name: Dot Accuracy@5
253
  - type: dot_accuracy@10
254
+ value: 0.6648996592199924
255
  name: Dot Accuracy@10
256
  - type: dot_precision@1
257
+ value: 0.3934115865202575
258
  name: Dot Precision@1
259
  - type: dot_precision@3
260
+ value: 0.1770793891202827
261
  name: Dot Precision@3
262
  - type: dot_precision@5
263
+ value: 0.11798561151079137
264
  name: Dot Precision@5
265
  - type: dot_precision@10
266
+ value: 0.06648996592199924
267
  name: Dot Precision@10
268
  - type: dot_recall@1
269
+ value: 0.3934115865202575
270
  name: Dot Recall@1
271
  - type: dot_recall@3
272
+ value: 0.5312381673608482
273
  name: Dot Recall@3
274
  - type: dot_recall@5
275
+ value: 0.5899280575539568
276
  name: Dot Recall@5
277
  - type: dot_recall@10
278
+ value: 0.6648996592199924
279
  name: Dot Recall@10
280
  - type: dot_ndcg@10
281
+ value: 0.5224316548033627
282
  name: Dot Ndcg@10
283
  - type: dot_mrr@10
284
+ value: 0.4775905591316421
285
  name: Dot Mrr@10
286
  - type: dot_map@100
287
+ value: 0.485319730256097
288
  name: Dot Map@100
289
  ---
290
 
291
+ # SentenceTransformer
292
 
293
+ This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
294
 
295
  ## Model Details
296
 
297
  ### Model Description
298
  - **Model Type:** Sentence Transformer
299
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
300
  - **Maximum Sequence Length:** 350 tokens
301
  - **Output Dimensionality:** 768 tokens
302
  - **Similarity Function:** Cosine Similarity
 
385
 
386
  | Metric | Value |
387
  |:--------------------|:-----------|
388
+ | cosine_accuracy@1 | 0.3953 |
389
+ | cosine_accuracy@3 | 0.5343 |
390
+ | cosine_accuracy@5 | 0.5914 |
391
+ | cosine_accuracy@10 | 0.6657 |
392
+ | cosine_precision@1 | 0.3953 |
393
+ | cosine_precision@3 | 0.1781 |
394
+ | cosine_precision@5 | 0.1183 |
395
+ | cosine_precision@10 | 0.0666 |
396
+ | cosine_recall@1 | 0.3953 |
397
+ | cosine_recall@3 | 0.5343 |
398
+ | cosine_recall@5 | 0.5914 |
399
+ | cosine_recall@10 | 0.6657 |
400
+ | cosine_ndcg@10 | 0.5241 |
401
+ | cosine_mrr@10 | 0.4795 |
402
+ | **cosine_map@100** | **0.4872** |
403
+ | dot_accuracy@1 | 0.3934 |
404
+ | dot_accuracy@3 | 0.5312 |
405
+ | dot_accuracy@5 | 0.5899 |
406
+ | dot_accuracy@10 | 0.6649 |
407
+ | dot_precision@1 | 0.3934 |
408
+ | dot_precision@3 | 0.1771 |
409
+ | dot_precision@5 | 0.118 |
410
+ | dot_precision@10 | 0.0665 |
411
+ | dot_recall@1 | 0.3934 |
412
+ | dot_recall@3 | 0.5312 |
413
+ | dot_recall@5 | 0.5899 |
414
+ | dot_recall@10 | 0.6649 |
415
+ | dot_ndcg@10 | 0.5224 |
416
+ | dot_mrr@10 | 0.4776 |
417
+ | dot_map@100 | 0.4853 |
418
 
419
  <!--
420
  ## Bias, Risks and Limitations
 
611
  ### Training Logs
612
  | Epoch | Step | Training Loss | loss | msmarco-distilbert-base-v2_cosine_map@100 |
613
  |:----------:|:--------:|:-------------:|:----------:|:-----------------------------------------:|
614
+ | 0 | 0 | - | - | 0.4899 |
615
+ | 0.1453 | 100 | 0.0787 | - | - |
616
+ | 0.2907 | 200 | 0.0503 | - | - |
617
+ | 0.4360 | 300 | 0.0529 | - | - |
618
+ | 0.5814 | 400 | 0.0636 | - | - |
619
+ | 0.7267 | 500 | 0.0783 | - | - |
620
+ | 0.8721 | 600 | 0.0765 | - | - |
621
+ | 1.0131 | 697 | - | 0.2284 | - |
622
+ | 1.0044 | 700 | 0.0776 | - | - |
623
+ | 1.1497 | 800 | 0.0624 | - | - |
624
+ | 1.2951 | 900 | 0.0289 | - | - |
625
+ | 1.4404 | 1000 | 0.0244 | - | - |
626
+ | 1.5858 | 1100 | 0.0256 | - | - |
627
+ | 1.7311 | 1200 | 0.0364 | - | - |
628
+ | 1.8765 | 1300 | 0.0334 | - | - |
629
+ | 2.0131 | 1394 | - | 0.2175 | - |
630
+ | 2.0087 | 1400 | 0.0342 | - | - |
631
+ | 2.1541 | 1500 | 0.0274 | - | - |
632
+ | 2.2994 | 1600 | 0.0153 | - | - |
633
+ | 2.4448 | 1700 | 0.0167 | - | - |
634
+ | 2.5901 | 1800 | 0.0178 | - | - |
635
+ | 2.7355 | 1900 | 0.0221 | - | - |
636
+ | 2.8808 | 2000 | 0.0227 | - | - |
637
+ | **2.9738** | **2064** | **-** | **0.1821** | **0.4872** |
638
 
639
  * The bold row denotes the saved checkpoint.
640
 
config.json CHANGED
@@ -1,5 +1,5 @@
1
  {
2
- "_name_or_path": "sentence-transformers/msmarco-distilbert-base-v2",
3
  "activation": "gelu",
4
  "architectures": [
5
  "DistilBertModel"
 
1
  {
2
+ "_name_or_path": "models/msmarco-distilbert-base-v2_triplet_legal/final",
3
  "activation": "gelu",
4
  "architectures": [
5
  "DistilBertModel"
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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  size 265462608
 
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tokenizer_config.json CHANGED
@@ -46,12 +46,19 @@
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  "do_basic_tokenize": true,
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  "do_lower_case": true,
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  "mask_token": "[MASK]",
 
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  "model_max_length": 350,
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  "never_split": null,
 
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  "pad_token": "[PAD]",
 
 
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  "sep_token": "[SEP]",
 
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  "strip_accents": null,
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  "tokenize_chinese_chars": true,
55
  "tokenizer_class": "DistilBertTokenizer",
 
 
56
  "unk_token": "[UNK]"
57
  }
 
46
  "do_basic_tokenize": true,
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  "do_lower_case": true,
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  "mask_token": "[MASK]",
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+ "max_length": 350,
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  "model_max_length": 350,
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  "never_split": null,
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+ "pad_to_multiple_of": null,
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  "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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  "sep_token": "[SEP]",
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+ "stride": 0,
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  "strip_accents": null,
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  "tokenize_chinese_chars": true,
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  "tokenizer_class": "DistilBertTokenizer",
61
+ "truncation_side": "right",
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+ "truncation_strategy": "longest_first",
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  "unk_token": "[UNK]"
64
  }