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Merge branch 'main' of hf.co:spaces/JPBianchi/vectorsearch
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- data/.DS_Store +0 -0
- data/golden_100.json +0 -0
- data/impact_theory_data.json +3 -0
- data/training_data_300.json +0 -0
- data/validation_data_100.json +0 -0
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- models/finetuned-all-MiniLM-L6-v2-300/README.md +91 -0
- models/finetuned-all-MiniLM-L6-v2-300/config.json +26 -0
- models/finetuned-all-MiniLM-L6-v2-300/config_sentence_transformers.json +7 -0
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- models/finetuned-all-MiniLM-L6-v2-300/pytorch_model.bin +3 -0
- models/finetuned-all-MiniLM-L6-v2-300/sentence_bert_config.json +4 -0
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- models/finetuned-all-MiniLM-L6-v2-300/vocab.txt +0 -0
- models/local.txt +1 -0
- models/models/.DS_Store +0 -0
- models/models/all-MiniLM-L6-v2/.DS_Store +0 -0
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- models/models/all-MiniLM-L6-v2/README.md +176 -0
- models/models/all-MiniLM-L6-v2/config.json +26 -0
- models/models/all-MiniLM-L6-v2/config_sentence_transformers.json +7 -0
- models/models/all-MiniLM-L6-v2/modules.json +20 -0
- models/models/all-MiniLM-L6-v2/pytorch_model.bin +3 -0
- models/models/all-MiniLM-L6-v2/sentence_bert_config.json +4 -0
- models/models/all-MiniLM-L6-v2/special_tokens_map.json +7 -0
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- models/models/all-MiniLM-L6-v2/tokenizer_config.json +22 -0
- models/models/all-MiniLM-L6-v2/vocab.txt +0 -0
- models/models/all-mpnet-base-v2/.DS_Store +0 -0
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- models/models/all-mpnet-base-v2/sentence_bert_config.json +4 -0
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- models/models/all-mpnet-base-v2/tokenizer.json +0 -0
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- models/models/all-mpnet-base-v2/vocab.txt +0 -0
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- models/models/finetuned-all-mpnet-base-v2-300/1_Pooling/config.json +7 -0
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- models/models/finetuned-all-mpnet-base-v2-300/config.json +24 -0
- models/models/finetuned-all-mpnet-base-v2-300/config_sentence_transformers.json +7 -0
data/.DS_Store
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data/golden_100.json
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data/impact_theory_data.json
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data/training_data_300.json
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data/validation_data_100.json
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models/.DS_Store
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models/finetuned-all-MiniLM-L6-v2-300/.DS_Store
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models/finetuned-all-MiniLM-L6-v2-300/1_Pooling/config.json
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models/finetuned-all-MiniLM-L6-v2-300/README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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---
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# {MODEL_NAME}
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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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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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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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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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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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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 10 with parameters:
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```
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{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
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```
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{'scale': 20.0, 'similarity_fct': 'cos_sim'}
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs": 10,
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"evaluation_steps": 50,
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"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 10,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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(2): Normalize()
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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models/finetuned-all-MiniLM-L6-v2-300/config.json
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"architectures": [
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"BertModel"
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],
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.33.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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models/finetuned-all-MiniLM-L6-v2-300/config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.6.1",
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"pytorch": "1.8.1"
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}
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}
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models/finetuned-all-MiniLM-L6-v2-300/modules.json
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models/finetuned-all-MiniLM-L6-v2-300/sentence_bert_config.json
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models/finetuned-all-MiniLM-L6-v2-300/tokenizer.json
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models/finetuned-all-MiniLM-L6-v2-300/tokenizer_config.json
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models/finetuned-all-MiniLM-L6-v2-300/vocab.txt
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models/local.txt
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Used to let streamlit if it's running locally or online
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models/models/.DS_Store
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models/models/all-MiniLM-L6-v2/.DS_Store
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models/models/all-MiniLM-L6-v2/1_Pooling/config.json
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models/models/all-MiniLM-L6-v2/README.md
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---
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2 |
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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language: en
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license: apache-2.0
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datasets:
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- s2orc
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- flax-sentence-embeddings/stackexchange_xml
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- ms_marco
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- gooaq
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- yahoo_answers_topics
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- code_search_net
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- search_qa
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- eli5
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- snli
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- multi_nli
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- wikihow
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- natural_questions
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- trivia_qa
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- embedding-data/sentence-compression
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- embedding-data/flickr30k-captions
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- embedding-data/altlex
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- embedding-data/simple-wiki
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- embedding-data/QQP
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- embedding-data/SPECTER
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- embedding-data/PAQ_pairs
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- embedding-data/WikiAnswers
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---
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# all-MiniLM-L6-v2
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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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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Usage (HuggingFace Transformers)
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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.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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import torch.nn.functional as F
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|
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
|
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
|
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tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
|
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model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
|
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|
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# Tokenize sentences
|
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
|
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
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|
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# Normalize embeddings
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sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
|
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|
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print("Sentence embeddings:")
|
91 |
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print(sentence_embeddings)
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```
|
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)
|
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|
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------
|
99 |
+
|
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## 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
|
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+
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.
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## Intended uses
|
112 |
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|
113 |
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Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
|
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the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
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By default, input text longer than 256 word pieces is truncated.
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|
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## Training procedure
|
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+
|
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### Pre-training
|
122 |
+
|
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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.
|
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+
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### Fine-tuning
|
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|
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We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
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We then apply the cross entropy loss by comparing with true pairs.
|
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|
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#### Hyper parameters
|
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|
132 |
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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
|
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+
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
|
135 |
+
|
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#### Training data
|
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+
|
138 |
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We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
|
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We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
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|
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| 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** |
|
models/models/all-MiniLM-L6-v2/config.json
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+
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|
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|
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|
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|
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|
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|
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|
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|
models/models/all-MiniLM-L6-v2/config_sentence_transformers.json
ADDED
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{
|
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"__version__": {
|
3 |
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"sentence_transformers": "2.0.0",
|
4 |
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"transformers": "4.6.1",
|
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"pytorch": "1.8.1"
|
6 |
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}
|
7 |
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|
models/models/all-MiniLM-L6-v2/modules.json
ADDED
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[
|
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{
|
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|
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"type": "sentence_transformers.models.Transformer"
|
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|
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{
|
9 |
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"idx": 1,
|
10 |
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"name": "1",
|
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"path": "1_Pooling",
|
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"type": "sentence_transformers.models.Pooling"
|
13 |
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},
|
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{
|
15 |
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"idx": 2,
|
16 |
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"name": "2",
|
17 |
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"path": "2_Normalize",
|
18 |
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"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
models/models/all-MiniLM-L6-v2/pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:72ea817e757ec2f5aea799d9be2f38ea29fadbeadcc63952feacc79524ccd8c5
|
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size 90885737
|
models/models/all-MiniLM-L6-v2/sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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{
|
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"max_seq_length": 256,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
models/models/all-MiniLM-L6-v2/special_tokens_map.json
ADDED
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{
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"cls_token": "[CLS]",
|
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"mask_token": "[MASK]",
|
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"pad_token": "[PAD]",
|
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"sep_token": "[SEP]",
|
6 |
+
"unk_token": "[UNK]"
|
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+
}
|
models/models/all-MiniLM-L6-v2/tokenizer.json
ADDED
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models/models/all-MiniLM-L6-v2/tokenizer_config.json
ADDED
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|
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+
{
|
2 |
+
"clean_up_tokenization_spaces": true,
|
3 |
+
"cls_token": "[CLS]",
|
4 |
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"do_basic_tokenize": true,
|
5 |
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"do_lower_case": true,
|
6 |
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"mask_token": "[MASK]",
|
7 |
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"max_length": 128,
|
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"model_max_length": 512,
|
9 |
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"never_split": null,
|
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|
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|
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"pad_token_type_id": 0,
|
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"padding_side": "right",
|
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"sep_token": "[SEP]",
|
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"stride": 0,
|
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"strip_accents": null,
|
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"tokenize_chinese_chars": true,
|
18 |
+
"tokenizer_class": "BertTokenizer",
|
19 |
+
"truncation_side": "right",
|
20 |
+
"truncation_strategy": "longest_first",
|
21 |
+
"unk_token": "[UNK]"
|
22 |
+
}
|
models/models/all-MiniLM-L6-v2/vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
models/models/all-mpnet-base-v2/.DS_Store
ADDED
Binary file (6.15 kB). View file
|
|
models/models/all-mpnet-base-v2/1_Pooling/config.json
ADDED
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+
{
|
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 |
+
}
|
models/models/all-mpnet-base-v2/README.md
ADDED
@@ -0,0 +1,176 @@
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|
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-mpnet-base-v2
|
36 |
+
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.
|
37 |
+
|
38 |
+
## Usage (Sentence-Transformers)
|
39 |
+
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
|
40 |
+
|
41 |
+
```
|
42 |
+
pip install -U sentence-transformers
|
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-mpnet-base-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):
|
65 |
+
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
|
66 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
67 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
68 |
+
|
69 |
+
|
70 |
+
# Sentences we want sentence embeddings for
|
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-mpnet-base-v2')
|
75 |
+
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
|
76 |
+
|
77 |
+
# Tokenize sentences
|
78 |
+
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
79 |
+
|
80 |
+
# Compute token embeddings
|
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-mpnet-base-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 [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) 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 384 word pieces is truncated.
|
117 |
+
|
118 |
+
|
119 |
+
## Training procedure
|
120 |
+
|
121 |
+
### Pre-training
|
122 |
+
|
123 |
+
We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) 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** |
|
models/models/all-mpnet-base-v2/config.json
ADDED
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{
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"_name_or_path": "/Users/jpb2/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
|
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+
"architectures": [
|
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+
"MPNetModel"
|
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+
],
|
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"attention_probs_dropout_prob": 0.1,
|
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|
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|
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"hidden_act": "gelu",
|
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|
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"hidden_size": 768,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 3072,
|
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"layer_norm_eps": 1e-05,
|
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+
"max_position_embeddings": 514,
|
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+
"model_type": "mpnet",
|
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+
"num_attention_heads": 12,
|
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"num_hidden_layers": 12,
|
19 |
+
"pad_token_id": 1,
|
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"relative_attention_num_buckets": 32,
|
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+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.33.1",
|
23 |
+
"vocab_size": 30527
|
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+
}
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models/models/all-mpnet-base-v2/config_sentence_transformers.json
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{
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"__version__": {
|
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"sentence_transformers": "2.0.0",
|
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"transformers": "4.6.1",
|
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+
"pytorch": "1.8.1"
|
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+
}
|
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}
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models/models/all-mpnet-base-v2/modules.json
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[
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{
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"idx": 0,
|
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"name": "0",
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"path": "",
|
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"type": "sentence_transformers.models.Transformer"
|
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+
},
|
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+
{
|
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+
"idx": 1,
|
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+
"name": "1",
|
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+
"path": "1_Pooling",
|
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+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
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+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
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+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
models/models/all-mpnet-base-v2/pytorch_model.bin
ADDED
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:70be8a5840b262b79cbbad82c69c96f6476bddc373182a012f1bcb251865322b
|
3 |
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size 438009257
|
models/models/all-mpnet-base-v2/sentence_bert_config.json
ADDED
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|
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+
{
|
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+
"max_seq_length": 384,
|
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+
"do_lower_case": false
|
4 |
+
}
|
models/models/all-mpnet-base-v2/special_tokens_map.json
ADDED
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{
|
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"bos_token": "<s>",
|
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"cls_token": "<s>",
|
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+
"eos_token": "</s>",
|
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+
"mask_token": {
|
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+
"content": "<mask>",
|
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+
"lstrip": true,
|
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+
"normalized": false,
|
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+
"rstrip": false,
|
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+
"single_word": false
|
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+
},
|
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"pad_token": "<pad>",
|
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+
"sep_token": "</s>",
|
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+
"unk_token": "[UNK]"
|
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+
}
|
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ADDED
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models/models/all-mpnet-base-v2/tokenizer_config.json
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{
|
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"bos_token": "<s>",
|
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"clean_up_tokenization_spaces": true,
|
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+
"cls_token": "<s>",
|
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+
"do_lower_case": true,
|
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+
"eos_token": "</s>",
|
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+
"mask_token": "<mask>",
|
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+
"max_length": 128,
|
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+
"model_max_length": 512,
|
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+
"pad_to_multiple_of": null,
|
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"pad_token": "<pad>",
|
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"pad_token_type_id": 0,
|
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"padding_side": "right",
|
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+
"sep_token": "</s>",
|
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+
"stride": 0,
|
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+
"strip_accents": null,
|
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+
"tokenize_chinese_chars": true,
|
18 |
+
"tokenizer_class": "MPNetTokenizer",
|
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+
"truncation_side": "right",
|
20 |
+
"truncation_strategy": "longest_first",
|
21 |
+
"unk_token": "[UNK]"
|
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+
}
|
models/models/all-mpnet-base-v2/vocab.txt
ADDED
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|
models/models/finetuned-all-mpnet-base-v2-300/.DS_Store
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models/models/finetuned-all-mpnet-base-v2-300/1_Pooling/config.json
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{
|
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"word_embedding_dimension": 768,
|
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+
"pooling_mode_cls_token": false,
|
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+
"pooling_mode_mean_tokens": true,
|
5 |
+
"pooling_mode_max_tokens": false,
|
6 |
+
"pooling_mode_mean_sqrt_len_tokens": false
|
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+
}
|
models/models/finetuned-all-mpnet-base-v2-300/README.md
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|
1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
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 768 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}')
|
31 |
+
embeddings = model.encode(sentences)
|
32 |
+
print(embeddings)
|
33 |
+
```
|
34 |
+
|
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 |
+
## Training
|
45 |
+
The model was trained with the parameters:
|
46 |
+
|
47 |
+
**DataLoader**:
|
48 |
+
|
49 |
+
`torch.utils.data.dataloader.DataLoader` of length 10 with parameters:
|
50 |
+
```
|
51 |
+
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
|
52 |
+
```
|
53 |
+
|
54 |
+
**Loss**:
|
55 |
+
|
56 |
+
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
|
57 |
+
```
|
58 |
+
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
|
59 |
+
```
|
60 |
+
|
61 |
+
Parameters of the fit()-Method:
|
62 |
+
```
|
63 |
+
{
|
64 |
+
"epochs": 10,
|
65 |
+
"evaluation_steps": 50,
|
66 |
+
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
|
67 |
+
"max_grad_norm": 1,
|
68 |
+
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
|
69 |
+
"optimizer_params": {
|
70 |
+
"lr": 2e-05
|
71 |
+
},
|
72 |
+
"scheduler": "WarmupLinear",
|
73 |
+
"steps_per_epoch": null,
|
74 |
+
"warmup_steps": 10,
|
75 |
+
"weight_decay": 0.01
|
76 |
+
}
|
77 |
+
```
|
78 |
+
|
79 |
+
|
80 |
+
## Full Model Architecture
|
81 |
+
```
|
82 |
+
SentenceTransformer(
|
83 |
+
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
|
84 |
+
(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})
|
85 |
+
(2): Normalize()
|
86 |
+
)
|
87 |
+
```
|
88 |
+
|
89 |
+
## Citing & Authors
|
90 |
+
|
91 |
+
<!--- Describe where people can find more information -->
|
models/models/finetuned-all-mpnet-base-v2-300/config.json
ADDED
@@ -0,0 +1,24 @@
|
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|
1 |
+
{
|
2 |
+
"_name_or_path": "/Users/jpb2/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
|
3 |
+
"architectures": [
|
4 |
+
"MPNetModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"bos_token_id": 0,
|
8 |
+
"eos_token_id": 2,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 768,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 3072,
|
14 |
+
"layer_norm_eps": 1e-05,
|
15 |
+
"max_position_embeddings": 514,
|
16 |
+
"model_type": "mpnet",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 12,
|
19 |
+
"pad_token_id": 1,
|
20 |
+
"relative_attention_num_buckets": 32,
|
21 |
+
"torch_dtype": "float32",
|
22 |
+
"transformers_version": "4.33.1",
|
23 |
+
"vocab_size": 30527
|
24 |
+
}
|
models/models/finetuned-all-mpnet-base-v2-300/config_sentence_transformers.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.0.0",
|
4 |
+
"transformers": "4.6.1",
|
5 |
+
"pytorch": "1.8.1"
|
6 |
+
}
|
7 |
+
}
|