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ArchitRastogi
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BGE-Small-LegalEmbeddings-USCode

Sentence Similarity
Transformers
TensorBoard
Safetensors
sentence-transformers
English
bert
feature-extraction
embeddings
legal
USCode
Eval Results
text-embeddings-inference
Model card Files
xet
Metrics Community
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v1 of model

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Files changed (33) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +56 -3
  3. checkpoint-500/1_Pooling/config.json +7 -0
  4. checkpoint-500/README.md +56 -0
  5. checkpoint-500/config.json +31 -0
  6. checkpoint-500/config_sentence_transformers.json +7 -0
  7. checkpoint-500/model.safetensors +3 -0
  8. checkpoint-500/modules.json +20 -0
  9. checkpoint-500/optimizer.pt +3 -0
  10. checkpoint-500/rng_state.pth +3 -0
  11. checkpoint-500/scheduler.pt +3 -0
  12. checkpoint-500/sentence_bert_config.json +4 -0
  13. checkpoint-500/special_tokens_map.json +37 -0
  14. checkpoint-500/tokenizer.json +0 -0
  15. checkpoint-500/tokenizer_config.json +57 -0
  16. checkpoint-500/trainer_state.json +319 -0
  17. checkpoint-500/training_args.bin +3 -0
  18. checkpoint-500/vocab.txt +0 -0
  19. config.json +31 -0
  20. config_sentence_transformers.json +7 -0
  21. model.safetensors +3 -0
  22. modules.json +20 -0
  23. runs/Dec01_18-32-07_00f6574a3601/events.out.tfevents.1701455531.00f6574a3601.5224.0 +3 -0
  24. runs/Dec01_18-32-59_00f6574a3601/events.out.tfevents.1701455585.00f6574a3601.5480.0 +3 -0
  25. runs/Dec01_18-34-01_00f6574a3601/events.out.tfevents.1701455646.00f6574a3601.5764.0 +3 -0
  26. runs/Dec01_18-36-58_00f6574a3601/events.out.tfevents.1701455823.00f6574a3601.6521.0 +3 -0
  27. runs/Dec01_21-15-03_e26823ad6ad8/events.out.tfevents.1701465322.e26823ad6ad8.855.0 +3 -0
  28. sentence_bert_config.json +4 -0
  29. special_tokens_map.json +37 -0
  30. tokenizer.json +0 -0
  31. tokenizer_config.json +57 -0
  32. training_args.bin +3 -0
  33. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": true,
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README.md CHANGED
@@ -1,3 +1,56 @@
1
- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
2
+ pipeline_tag: sentence-similarity
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+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
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+
8
+ ---
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+
10
+ # {MODEL_NAME}
11
+
12
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
14
+ <!--- Describe your model here -->
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+
16
+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
20
+ ```
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+ pip install -U sentence-transformers
22
+ ```
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+
24
+ Then you can use the model like this:
25
+
26
+ ```python
27
+ from sentence_transformers import SentenceTransformer
28
+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
30
+ model = SentenceTransformer('{MODEL_NAME}')
31
+ embeddings = model.encode(sentences)
32
+ print(embeddings)
33
+ ```
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+
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+
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+
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+ ## Evaluation Results
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+
39
+ <!--- Describe how your model was evaluated -->
40
+
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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+
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+
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+
45
+ ## Full Model Architecture
46
+ ```
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+ SentenceTransformer(
48
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
49
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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+ (2): Normalize()
51
+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
checkpoint-500/1_Pooling/config.json ADDED
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checkpoint-500/README.md ADDED
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+ ---
2
+ pipeline_tag: sentence-similarity
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+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+
8
+ ---
9
+
10
+ # {MODEL_NAME}
11
+
12
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
13
+
14
+ <!--- Describe your model here -->
15
+
16
+ ## Usage (Sentence-Transformers)
17
+
18
+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
19
+
20
+ ```
21
+ pip install -U sentence-transformers
22
+ ```
23
+
24
+ Then you can use the model like this:
25
+
26
+ ```python
27
+ from sentence_transformers import SentenceTransformer
28
+ sentences = ["This is an example sentence", "Each sentence is converted"]
29
+
30
+ model = SentenceTransformer('{MODEL_NAME}')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
35
+
36
+
37
+ ## Evaluation Results
38
+
39
+ <!--- Describe how your model was evaluated -->
40
+
41
+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
42
+
43
+
44
+
45
+ ## Full Model Architecture
46
+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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+ (2): Normalize()
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+ )
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+ ```
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+
54
+ ## Citing & Authors
55
+
56
+ <!--- Describe where people can find more information -->
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