nreimers commited on
Commit
af122d2
1 Parent(s): a2a36cb

Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
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 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pipeline_tag: sentence-similarity
3
+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+ - transformers
8
+ ---
9
+
10
+ # sentence-transformers/paraphrase-multilingual-mpnet-base-v2
11
+
12
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
13
+
14
+
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('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
31
+ embeddings = model.encode(sentences)
32
+ print(embeddings)
33
+ ```
34
+
35
+
36
+
37
+ ## Usage (HuggingFace Transformers)
38
+ 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.
39
+
40
+ ```python
41
+ from transformers import AutoTokenizer, AutoModel
42
+ import torch
43
+
44
+
45
+ #Mean Pooling - Take attention mask into account for correct averaging
46
+ def mean_pooling(model_output, attention_mask):
47
+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
48
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
49
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
50
+
51
+
52
+ # Sentences we want sentence embeddings for
53
+ sentences = ['This is an example sentence', 'Each sentence is converted']
54
+
55
+ # Load model from HuggingFace Hub
56
+ tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
57
+ model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
58
+
59
+ # Tokenize sentences
60
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
61
+
62
+ # Compute token embeddings
63
+ with torch.no_grad():
64
+ model_output = model(**encoded_input)
65
+
66
+ # Perform pooling. In this case, max pooling.
67
+ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
68
+
69
+ print("Sentence embeddings:")
70
+ print(sentence_embeddings)
71
+ ```
72
+
73
+
74
+
75
+ ## Evaluation Results
76
+
77
+
78
+
79
+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
80
+
81
+
82
+
83
+ ## Full Model Architecture
84
+ ```
85
+ SentenceTransformer(
86
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
87
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
88
+ )
89
+ ```
90
+
91
+ ## Citing & Authors
92
+
93
+ This model was trained by [sentence-transformers](https://www.sbert.net/).
94
+
95
+ 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):
96
+ ```bibtex
97
+ @inproceedings{reimers-2019-sentence-bert,
98
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
99
+ author = "Reimers, Nils and Gurevych, Iryna",
100
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
101
+ month = "11",
102
+ year = "2019",
103
+ publisher = "Association for Computational Linguistics",
104
+ url = "http://arxiv.org/abs/1908.10084",
105
+ }
106
+ ```
config.json CHANGED
@@ -1,5 +1,5 @@
1
  {
2
- "_name_or_path": "xlm-roberta-base",
3
  "architectures": [
4
  "XLMRobertaModel"
5
  ],
@@ -20,7 +20,7 @@
20
  "output_past": true,
21
  "pad_token_id": 1,
22
  "position_embedding_type": "absolute",
23
- "transformers_version": "4.6.1",
24
  "type_vocab_size": 1,
25
  "use_cache": true,
26
  "vocab_size": 250002
 
1
  {
2
+ "_name_or_path": "old_models/paraphrase-multilingual-mpnet-base-v2/0_Transformer",
3
  "architectures": [
4
  "XLMRobertaModel"
5
  ],
 
20
  "output_past": true,
21
  "pad_token_id": 1,
22
  "position_embedding_type": "absolute",
23
+ "transformers_version": "4.7.0",
24
  "type_vocab_size": 1,
25
  "use_cache": true,
26
  "vocab_size": 250002
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 sha256:30d3f5d8253bf38106a470535fdf7e9720517aa587d6ad0d6ef54e6486f00eb0
3
- size 1112261175
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:29d10eabb079d11355d4405789dd9d43e229ccbc47bc2581d0639edaa1721380
3
+ size 1112253233
tokenizer_config.json CHANGED
@@ -1 +1 @@
1
- {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "output/make-multilingual-large-paraphrase-mpnet-base-v2-2021-05-21_08-33-36/0_Transformer"}
 
1
+ {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "old_models/paraphrase-multilingual-mpnet-base-v2/0_Transformer"}