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Sentence Similarity
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TensorBoard
Safetensors
English
bert
feature-extraction
text-embeddings-inference
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  1. README.md +108 -39
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README.md CHANGED
@@ -1,21 +1,41 @@
1
  ---
2
  pipeline_tag: sentence-similarity
3
- license: apache-2.0
4
  tags:
5
  - sentence-transformers
6
  - feature-extraction
7
  - sentence-similarity
8
- - transformers
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  ---
10
 
11
- # sentence-transformers/paraphrase-MiniLM-L6-v2
12
 
 
13
  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.
14
 
15
-
16
-
17
  ## Usage (Sentence-Transformers)
18
-
19
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
20
 
21
  ```
@@ -23,25 +43,22 @@ pip install -U sentence-transformers
23
  ```
24
 
25
  Then you can use the model like this:
26
-
27
  ```python
28
  from sentence_transformers import SentenceTransformer
29
  sentences = ["This is an example sentence", "Each sentence is converted"]
30
 
31
- model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')
32
  embeddings = model.encode(sentences)
33
  print(embeddings)
34
  ```
35
 
36
-
37
-
38
  ## Usage (HuggingFace Transformers)
39
  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.
40
 
41
  ```python
42
  from transformers import AutoTokenizer, AutoModel
43
  import torch
44
-
45
 
46
  #Mean Pooling - Take attention mask into account for correct averaging
47
  def mean_pooling(model_output, attention_mask):
@@ -54,8 +71,8 @@ def mean_pooling(model_output, attention_mask):
54
  sentences = ['This is an example sentence', 'Each sentence is converted']
55
 
56
  # Load model from HuggingFace Hub
57
- tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
58
- model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
59
 
60
  # Tokenize sentences
61
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -64,44 +81,96 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
64
  with torch.no_grad():
65
  model_output = model(**encoded_input)
66
 
67
- # Perform pooling. In this case, max pooling.
68
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
69
 
 
 
 
70
  print("Sentence embeddings:")
71
  print(sentence_embeddings)
72
  ```
73
 
 
74
 
 
75
 
76
- ## Evaluation Results
77
 
 
78
 
 
 
 
79
 
80
- 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-MiniLM-L6-v2)
 
 
 
81
 
 
82
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83
 
84
- ## Full Model Architecture
85
- ```
86
- SentenceTransformer(
87
- (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
88
- (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
89
- )
90
- ```
91
 
92
- ## Citing & Authors
93
-
94
- This model was trained by [sentence-transformers](https://www.sbert.net/).
95
-
96
- 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):
97
- ```bibtex
98
- @inproceedings{reimers-2019-sentence-bert,
99
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
100
- author = "Reimers, Nils and Gurevych, Iryna",
101
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
102
- month = "11",
103
- year = "2019",
104
- publisher = "Association for Computational Linguistics",
105
- url = "http://arxiv.org/abs/1908.10084",
106
- }
107
- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  pipeline_tag: sentence-similarity
 
3
  tags:
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
7
+ language: en
8
+ license: apache-2.0
9
+ datasets:
10
+ - s2orc
11
+ - flax-sentence-embeddings/stackexchange_xml
12
+ - ms_marco
13
+ - gooaq
14
+ - yahoo_answers_topics
15
+ - code_search_net
16
+ - search_qa
17
+ - eli5
18
+ - snli
19
+ - multi_nli
20
+ - wikihow
21
+ - natural_questions
22
+ - trivia_qa
23
+ - embedding-data/sentence-compression
24
+ - embedding-data/flickr30k-captions
25
+ - embedding-data/altlex
26
+ - embedding-data/simple-wiki
27
+ - embedding-data/QQP
28
+ - embedding-data/SPECTER
29
+ - embedding-data/PAQ_pairs
30
+ - embedding-data/WikiAnswers
31
+
32
  ---
33
 
 
34
 
35
+ # all-MiniLM-L6-v2
36
  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.
37
 
 
 
38
  ## Usage (Sentence-Transformers)
 
39
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
40
 
41
  ```
 
43
  ```
44
 
45
  Then you can use the model like this:
 
46
  ```python
47
  from sentence_transformers import SentenceTransformer
48
  sentences = ["This is an example sentence", "Each sentence is converted"]
49
 
50
+ model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
51
  embeddings = model.encode(sentences)
52
  print(embeddings)
53
  ```
54
 
 
 
55
  ## Usage (HuggingFace Transformers)
56
  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.
57
 
58
  ```python
59
  from transformers import AutoTokenizer, AutoModel
60
  import torch
61
+ import torch.nn.functional as F
62
 
63
  #Mean Pooling - Take attention mask into account for correct averaging
64
  def mean_pooling(model_output, attention_mask):
 
71
  sentences = ['This is an example sentence', 'Each sentence is converted']
72
 
73
  # Load model from HuggingFace Hub
74
+ tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
75
+ model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
76
 
77
  # Tokenize sentences
78
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
81
  with torch.no_grad():
82
  model_output = model(**encoded_input)
83
 
84
+ # Perform pooling
85
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
86
 
87
+ # Normalize embeddings
88
+ sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
89
+
90
  print("Sentence embeddings:")
91
  print(sentence_embeddings)
92
  ```
93
 
94
+ ## Evaluation Results
95
 
96
+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
97
 
98
+ ------
99
 
100
+ ## Background
101
 
102
+ The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
103
+ contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
104
+ 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
105
 
106
+ We developped this model during the
107
+ [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
108
+ organized by Hugging Face. We developped this model as part of the project:
109
+ [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
110
 
111
+ ## Intended uses
112
 
113
+ Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
114
+ the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
115
+
116
+ By default, input text longer than 256 word pieces is truncated.
117
+
118
+
119
+ ## Training procedure
120
+
121
+ ### Pre-training
122
+
123
+ We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
124
+
125
+ ### Fine-tuning
126
+
127
+ We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
128
+ We then apply the cross entropy loss by comparing with true pairs.
129
+
130
+ #### Hyper parameters
131
+
132
+ We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
133
+ We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
134
+ a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
135
+
136
+ #### Training data
137
+
138
+ We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
139
+ We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
140
 
 
 
 
 
 
 
 
141
 
142
+ | Dataset | Paper | Number of training tuples |
143
+ |--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
144
+ | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
145
+ | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
146
+ | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
147
+ | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
148
+ | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
149
+ | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
150
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
151
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
152
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
153
+ | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
154
+ | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
155
+ | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
156
+ | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
157
+ | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
158
+ | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
159
+ | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
160
+ | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
161
+ | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
162
+ | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
163
+ | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
164
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
165
+ | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
166
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
167
+ | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
168
+ | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
169
+ | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
170
+ | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
171
+ | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
172
+ | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
173
+ | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
174
+ | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
175
+ | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
176
+ | **Total** | | **1,170,060,424** |
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