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nreimers
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Update README.md a7d0fe1
1 ---
2 pipeline_tag: feature-extraction
3 license: apache-2.0
4 tags:
5 - sentence-transformers
6 - feature-extraction
7 - sentence-similarity
8 - transformers
9 ---
10
11 **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
12
13
14 # sentence-transformers/distilbert-base-nli-mean-tokens
15
16 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.
17
18
19
20 ## Usage (Sentence-Transformers)
21
22 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
23
24 ```
25 pip install -U sentence-transformers
26 ```
27
28 Then you can use the model like this:
29
30 ```python
31 from sentence_transformers import SentenceTransformer
32 sentences = ["This is an example sentence", "Each sentence is converted"]
33
34 model = SentenceTransformer('sentence-transformers/distilbert-base-nli-mean-tokens')
35 embeddings = model.encode(sentences)
36 print(embeddings)
37 ```
38
39
40
41 ## Usage (HuggingFace Transformers)
42 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.
43
44 ```python
45 from transformers import AutoTokenizer, AutoModel
46 import torch
47
48
49 #Mean Pooling - Take attention mask into account for correct averaging
50 def mean_pooling(model_output, attention_mask):
51 token_embeddings = model_output[0] #First element of model_output contains all token embeddings
52 input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
53 return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
54
55
56 # Sentences we want sentence embeddings for
57 sentences = ['This is an example sentence', 'Each sentence is converted']
58
59 # Load model from HuggingFace Hub
60 tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-base-nli-mean-tokens')
61 model = AutoModel.from_pretrained('sentence-transformers/distilbert-base-nli-mean-tokens')
62
63 # Tokenize sentences
64 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
65
66 # Compute token embeddings
67 with torch.no_grad():
68 model_output = model(**encoded_input)
69
70 # Perform pooling. In this case, max pooling.
71 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
72
73 print("Sentence embeddings:")
74 print(sentence_embeddings)
75 ```
76
77
78
79 ## Evaluation Results
80
81
82
83 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distilbert-base-nli-mean-tokens)
84
85
86
87 ## Full Model Architecture
88 ```
89 SentenceTransformer(
90 (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
91 (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})
92 )
93 ```
94
95 ## Citing & Authors
96
97 This model was trained by [sentence-transformers](https://www.sbert.net/).
98
99 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):
100 ```bibtex
101 @inproceedings{reimers-2019-sentence-bert,
102 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
103 author = "Reimers, Nils and Gurevych, Iryna",
104 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
105 month = "11",
106 year = "2019",
107 publisher = "Association for Computational Linguistics",
108 url = "http://arxiv.org/abs/1908.10084",
109 }
110 ```