nreimers commited on
Commit
b7d6cdc
1 Parent(s): 2dc74d7

Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 1024,
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
+ }
README.md ADDED
@@ -0,0 +1,121 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pipeline_tag: sentence-similarity
3
+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+ - transformers
8
+ - transformers
9
+ - transformers
10
+ - transformers
11
+ - transformers
12
+ - transformers
13
+ - transformers
14
+ - transformers
15
+ - transformers
16
+ - transformers
17
+ - transformers
18
+ - transformers
19
+ - transformers
20
+ - transformers
21
+ - transformers
22
+ - transformers
23
+ ---
24
+
25
+ # sentence-transformers/bert-large-nli-mean-tokens
26
+
27
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
28
+
29
+
30
+
31
+ ## Usage (Sentence-Transformers)
32
+
33
+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
34
+
35
+ ```
36
+ pip install -U sentence-transformers
37
+ ```
38
+
39
+ Then you can use the model like this:
40
+
41
+ ```python
42
+ from sentence_transformers import SentenceTransformer
43
+ sentences = ["This is an example sentence", "Each sentence is converted"]
44
+
45
+ model = SentenceTransformer('sentence-transformers/bert-large-nli-mean-tokens')
46
+ embeddings = model.encode(sentences)
47
+ print(embeddings)
48
+ ```
49
+
50
+
51
+
52
+ ## Usage (HuggingFace Transformers)
53
+ Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
54
+
55
+ ```python
56
+ from transformers import AutoTokenizer, AutoModel
57
+ import torch
58
+
59
+
60
+ #Mean Pooling - Take attention mask into account for correct averaging
61
+ def mean_pooling(model_output, attention_mask):
62
+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
63
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
64
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
65
+
66
+
67
+ # Sentences we want sentence embeddings for
68
+ sentences = ['This is an example sentence', 'Each sentence is converted']
69
+
70
+ # Load model from HuggingFace Hub
71
+ tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-large-nli-mean-tokens')
72
+ model = AutoModel.from_pretrained('sentence-transformers/bert-large-nli-mean-tokens')
73
+
74
+ # Tokenize sentences
75
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
76
+
77
+ # Compute token embeddings
78
+ with torch.no_grad():
79
+ model_output = model(**encoded_input)
80
+
81
+ # Perform pooling. In this case, max pooling.
82
+ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
83
+
84
+ print("Sentence embeddings:")
85
+ print(sentence_embeddings)
86
+ ```
87
+
88
+
89
+
90
+ ## Evaluation Results
91
+
92
+
93
+
94
+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-large-nli-mean-tokens)
95
+
96
+
97
+
98
+ ## Full Model Architecture
99
+ ```
100
+ SentenceTransformer(
101
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
102
+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
103
+ )
104
+ ```
105
+
106
+ ## Citing & Authors
107
+
108
+ This model was trained by [sentence-transformers](https://www.sbert.net/).
109
+
110
+ If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
111
+ ```bibtex
112
+ @inproceedings{reimers-2019-sentence-bert,
113
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
114
+ author = "Reimers, Nils and Gurevych, Iryna",
115
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
116
+ month = "11",
117
+ year = "2019",
118
+ publisher = "Association for Computational Linguistics",
119
+ url = "http://arxiv.org/abs/1908.10084",
120
+ }
121
+ ```
config.json CHANGED
@@ -1,4 +1,5 @@
1
  {
 
2
  "architectures": [
3
  "BertModel"
4
  ],
@@ -15,6 +16,9 @@
15
  "num_attention_heads": 16,
16
  "num_hidden_layers": 24,
17
  "pad_token_id": 0,
 
 
18
  "type_vocab_size": 2,
 
19
  "vocab_size": 30522
20
- }
 
1
  {
2
+ "_name_or_path": "old_models/bert-large-nli-mean-tokens/0_BERT",
3
  "architectures": [
4
  "BertModel"
5
  ],
 
16
  "num_attention_heads": 16,
17
  "num_hidden_layers": 24,
18
  "pad_token_id": 0,
19
+ "position_embedding_type": "absolute",
20
+ "transformers_version": "4.7.0",
21
  "type_vocab_size": 2,
22
+ "use_cache": true,
23
  "vocab_size": 30522
24
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.0.0",
4
+ "transformers": "4.7.0",
5
+ "pytorch": "1.9.0+cu102"
6
+ }
7
+ }
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ ]
pytorch_model.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:a801e3fefea1d989998e289ade7fe9978996b604cc2b83c6579ca3591c755a67
3
- size 1340703974
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e7277f784e03dd6c3076faf9750dee7faa3ddc6c234023a427f528409615854c
3
+ size 1340718961
sentence_bert_config.json CHANGED
@@ -1,3 +1,4 @@
1
  {
2
- "max_seq_length": 128
 
3
  }
 
1
  {
2
+ "max_seq_length": 128,
3
+ "do_lower_case": false
4
  }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": "old_models/bert-large-nli-mean-tokens/0_BERT/special_tokens_map.json", "name_or_path": "old_models/bert-large-nli-mean-tokens/0_BERT", "do_basic_tokenize": true, "never_split": null}