Commit
•
8e7ea61
1
Parent(s):
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Upload folder using huggingface_hub
Browse files- 1_Pooling/config.json +9 -0
- README.md +166 -0
- added_tokens.json +9 -0
- config.json +28 -0
- config_sentence_transformers.json +7 -0
- modules.json +14 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +18 -0
- tokenizer.json +0 -0
- tokenizer_config.json +79 -0
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,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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}
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README.md
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---
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pipeline_tag: sentence-similarity
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language: fr
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license: apache-2.0
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datasets:
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- unicamp-dl/mmarco
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metrics:
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- recall
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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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- transformers
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---
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# biencoder-camembert-L6-mmarcoFR
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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. The model was trained on the **French** portion of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
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## Usage
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***
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#### 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('antoinelouis/biencoder-camembert-L6-mmarcoFR')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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#### 🤗 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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#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('antoinelouis/biencoder-camembert-L6-mmarcoFR')
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model = AutoModel.from_pretrained('antoinelouis/biencoder-camembert-L6-mmarcoFR')
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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. In this case, mean pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation
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***
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<!--- Describe how your model was evaluated -->
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Below, we compared its results with other biencoder models fine-tuned on the same dataset:
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| | model | MRR@10 | NDCG@10 | MAP@10 | Recall@10 | Recall@100 (↑) | Recall@500 |
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|---:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------:|----------:|---------:|------------:|-------------:|-------------:|
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| 0 | [biencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-camembert-base-mmarcoFR) | 28.53 | 33.72 | 27.93 | 51.46 | 77.82 | 89.13 |
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| 1 | [biencoder-all-mpnet-base-v2-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-all-mpnet-base-v2-mmarcoFR) | 28.04 | 33.28 | 27.5 | 51.07 | 77.68 | 88.67 |
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| 2 | [biencoder-multi-qa-mpnet-base-cos-v1-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-multi-qa-mpnet-base-cos-v1-mmarcoFR) | 27.6 | 32.92 | 27.09 | 50.97 | 77.41 | 87.79 |
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| 3 | [biencoder-sentence-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-sentence-camembert-base-mmarcoFR) | 27.63 | 32.7 | 27.01 | 50.1 | 76.85 | 88.73 |
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| 4 | [biencoder-distilcamembert-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-distilcamembert-base-mmarcoFR) | 26.8 | 31.87 | 26.23 | 49.2 | 76.44 | 87.87 |
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| 5 | [biencoder-mpnet-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mpnet-base-mmarcoFR) | 27.2 | 32.22 | 26.63 | 49.41 | 75.71 | 86.88 |
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| 6 | [biencoder-multi-qa-distilbert-cos-v1-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-multi-qa-distilbert-cos-v1-mmarcoFR) | 26.36 | 31.26 | 25.82 | 47.93 | 75.42 | 86.78 |
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| 7 | [biencoder-bert-base-uncased-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-bert-base-uncased-mmarcoFR) | 26.3 | 31.14 | 25.74 | 47.67 | 74.57 | 86.33 |
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| 8 | [biencoder-msmarco-distilbert-cos-v5-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-msmarco-distilbert-cos-v5-mmarcoFR) | 25.75 | 30.63 | 25.24 | 47.22 | 73.96 | 85.64 |
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| 9 | [biencoder-all-distilroberta-v1-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-all-distilroberta-v1-mmarcoFR) | 26.17 | 30.91 | 25.67 | 47.06 | 73.5 | 85.69 |
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| 10 | [biencoder-all-MiniLM-L6-v2-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-all-MiniLM-L6-v2-mmarcoFR) | 25.49 | 30.39 | 24.99 | 47.1 | 73.48 | 86.09 |
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| 11 | [biencoder-distilbert-base-uncased-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-distilbert-base-uncased-mmarcoFR) | 25.18 | 29.83 | 24.64 | 45.77 | 73.16 | 85.13 |
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| 12 | [biencoder-msmarco-MiniLM-L12-cos-v5-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-msmarco-MiniLM-L12-cos-v5-mmarcoFR) | 26.22 | 30.99 | 25.69 | 47.29 | 73.09 | 84.95 |
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| 13 | [biencoder-roberta-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-roberta-base-mmarcoFR) | 25.94 | 30.72 | 25.43 | 46.98 | 73.07 | 84.76 |
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| 14 | [biencoder-distiluse-base-multilingual-cased-v1-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-distiluse-base-multilingual-cased-v1-mmarcoFR) | 24.57 | 29.08 | 24.04 | 44.51 | 72.54 | 85.13 |
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| 15 | [biencoder-multi-qa-MiniLM-L6-cos-v1-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-multi-qa-MiniLM-L6-cos-v1-mmarcoFR) | 24.72 | 29.58 | 24.25 | 46.05 | 72.19 | 84.6 |
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| 16 | [biencoder-MiniLM-L12-H384-uncased-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-MiniLM-L12-H384-uncased-mmarcoFR) | 25.43 | 30.1 | 24.88 | 46.13 | 72.16 | 83.84 |
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| 17 | [biencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR) | 24.74 | 29.41 | 24.23 | 45.4 | 71.52 | 84.42 |
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| 18 | [biencoder-electra-base-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-electra-base-discriminator-mmarcoFR) | 24.77 | 29.37 | 24.21 | 45.2 | 70.84 | 83.25 |
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| 19 | [biencoder-bert-medium-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-bert-medium-mmarcoFR) | 23.86 | 28.56 | 23.39 | 44.47 | 70.57 | 83.58 |
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| 20 | [biencoder-msmarco-MiniLM-L6-cos-v5-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-msmarco-MiniLM-L6-cos-v5-mmarcoFR) | 24.39 | 28.96 | 23.91 | 44.58 | 70.36 | 82.88 |
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| 21 | [biencoder-distilroberta-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-distilroberta-base-mmarcoFR) | 23.94 | 28.44 | 23.46 | 43.77 | 70.08 | 82.86 |
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| 22 | [biencoder-camemberta-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-camemberta-base-mmarcoFR) | 24.78 | 29.24 | 24.23 | 44.58 | 69.59 | 82.18 |
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| 23 | [biencoder-electra-base-french-europeana-cased-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-electra-base-french-europeana-cased-discriminator-mmarcoFR) | 23.38 | 27.97 | 22.91 | 43.5 | 68.96 | 81.61 |
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| 24 | [biencoder-bert-small-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-bert-small-mmarcoFR) | 22.4 | 26.84 | 21.95 | 41.96 | 68.88 | 82.14 |
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| 25 | [biencoder-mMiniLM-L6-v2-mmarcoFR-v2-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mMiniLM-L6-v2-mmarcoFR-v2-mmarcoFR) | 22.87 | 27.26 | 22.37 | 42.3 | 68.78 | 81.39 |
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| 26 | [biencoder-MiniLM-L6-H384-uncased-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-MiniLM-L6-H384-uncased-mmarcoFR) | 22.86 | 27.34 | 22.41 | 42.62 | 68.4 | 81.54 |
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| 27 | [biencoder-deberta-v3-small-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-deberta-v3-small-mmarcoFR) | 22.44 | 26.84 | 21.97 | 41.84 | 68.17 | 80.9 |
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| 28 | [biencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR) | 22.29 | 26.57 | 21.8 | 41.25 | 66.78 | 79.83 |
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| 29 | [biencoder-bert-mini-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-bert-mini-mmarcoFR) | 20.06 | 24.09 | 19.66 | 37.78 | 64.27 | 77.39 |
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| 30 | [biencoder-electra-small-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-electra-small-discriminator-mmarcoFR) | 20.32 | 24.36 | 19.9 | 38.16 | 63.98 | 77.23 |
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| 31 | [biencoder-deberta-v3-xsmall-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-deberta-v3-xsmall-mmarcoFR) | 17.7 | 21.29 | 17.31 | 33.59 | 58.76 | 73.45 |
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| 32 | [biencoder-bert-tiny-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-bert-tiny-mmarcoFR) | 14.94 | 18.22 | 14.59 | 29.46 | 51.94 | 66.3 |
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| 33 | [biencoder-t5-small-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-t5-small-mmarcoFR) | 12.44 | 15.1 | 12.14 | 24.28 | 47.82 | 63.37 |
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| 34 | [biencoder-bert-small-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-bert-small-mmarcoFR) | 0.22 | 0.28 | 0.21 | 0.5 | 1.25 | 2.34 |
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## Training
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***
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#### Background
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We used the [output/-/camembert-L6](https://huggingface.co/output/-/camembert-L6) model and fine-tuned it on a 500K sentence pairs dataset in French. We used 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. Formally, we compute the cos similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss with a temperature of 0.05 by comparing with true pairs.
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#### Hyperparameters
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We trained the model on a single Tesla V100 GPU with 32GBs of memory during 40 epochs (i.e., 26.0k steps) using a batch size of 768. We used the AdamW optimizer with an initial learning rate of 2e-05, weight decay of 0.01, learning rate warmup over the first 2600 steps, and linear decay of the learning rate. The sequence length was limited to 128 tokens.
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#### Data
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We used the French version of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset to fine-tune our model. mMARCO is a multi-lingual machine-translated version of the MS MARCO dataset, a large-scale IR dataset comprising:
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- a corpus of 8.8M passages;
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- a training set of ~533k queries (with at least one relevant passage);
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- a development set of ~101k queries;
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- a smaller dev set of 6,980 queries (which is actually used for evaluation in most published works).
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Link: [https://ir-datasets.com/mmarco.html#mmarco/v2/fr/](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/)
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## Citation
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```bibtex
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@online{louis2023,
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author = 'Antoine Louis',
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title = 'biencoder-camembert-L6-mmarcoFR: A Biencoder Model Trained on French mMARCO',
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publisher = 'Hugging Face',
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month = 'may',
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year = '2023',
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url = 'https://huggingface.co/antoinelouis/biencoder-camembert-L6-mmarcoFR',
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}
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```
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{
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"</s>": 6,
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"</s>NOTUSED": 2,
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"<mask>": 32004,
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"<pad>": 1,
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"<s>": 5,
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"<s>NOTUSED": 0,
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"<unk>": 4
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}
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{
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"_name_or_path": "output/-/camembert-L6",
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"architectures": [
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"CamembertModel"
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],
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6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
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"bos_token_id": 5,
|
8 |
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"classifier_dropout": null,
|
9 |
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"eos_token_id": 6,
|
10 |
+
"hidden_act": "gelu",
|
11 |
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"hidden_dropout_prob": 0.1,
|
12 |
+
"hidden_size": 768,
|
13 |
+
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|
14 |
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"intermediate_size": 3072,
|
15 |
+
"layer_norm_eps": 1e-05,
|
16 |
+
"max_position_embeddings": 514,
|
17 |
+
"model_type": "camembert",
|
18 |
+
"num_attention_heads": 12,
|
19 |
+
"num_hidden_layers": 6,
|
20 |
+
"output_past": true,
|
21 |
+
"pad_token_id": 1,
|
22 |
+
"position_embedding_type": "absolute",
|
23 |
+
"torch_dtype": "float32",
|
24 |
+
"transformers_version": "4.35.0.dev0",
|
25 |
+
"type_vocab_size": 1,
|
26 |
+
"use_cache": true,
|
27 |
+
"vocab_size": 32005
|
28 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.2",
|
4 |
+
"transformers": "4.35.0.dev0",
|
5 |
+
"pytorch": "2.1.0+cu121"
|
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
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f22f406fc49dd88f5871effa7279645131f2d8517fd4ee29d4e60230340f2ea7
|
3 |
+
size 272413606
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 128,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
sentencepiece.bpe.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
|
3 |
+
size 810912
|
special_tokens_map.json
ADDED
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<s>NOTUSED",
|
4 |
+
"<pad>",
|
5 |
+
"</s>NOTUSED",
|
6 |
+
"<unk>",
|
7 |
+
"<s>",
|
8 |
+
"</s>",
|
9 |
+
"<mask>"
|
10 |
+
],
|
11 |
+
"bos_token": "<s>",
|
12 |
+
"cls_token": "<s>",
|
13 |
+
"eos_token": "</s>",
|
14 |
+
"mask_token": "<mask>",
|
15 |
+
"pad_token": "<pad>",
|
16 |
+
"sep_token": "</s>",
|
17 |
+
"unk_token": "<unk>"
|
18 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,79 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
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"content": "<s>NOTUSED",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "<pad>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "</s>NOTUSED",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"4": {
|
28 |
+
"content": "<unk>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"5": {
|
36 |
+
"content": "<s>",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"6": {
|
44 |
+
"content": "</s>",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"32004": {
|
52 |
+
"content": "<mask>",
|
53 |
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"lstrip": true,
|
54 |
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"normalized": false,
|
55 |
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"rstrip": false,
|
56 |
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"single_word": false,
|
57 |
+
"special": true
|
58 |
+
}
|
59 |
+
},
|
60 |
+
"additional_special_tokens": [
|
61 |
+
"<s>NOTUSED",
|
62 |
+
"<pad>",
|
63 |
+
"</s>NOTUSED",
|
64 |
+
"<unk>",
|
65 |
+
"<s>",
|
66 |
+
"</s>",
|
67 |
+
"<mask>"
|
68 |
+
],
|
69 |
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"bos_token": "<s>",
|
70 |
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"clean_up_tokenization_spaces": true,
|
71 |
+
"cls_token": "<s>",
|
72 |
+
"eos_token": "</s>",
|
73 |
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"mask_token": "<mask>",
|
74 |
+
"model_max_length": 128,
|
75 |
+
"pad_token": "<pad>",
|
76 |
+
"sep_token": "</s>",
|
77 |
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"tokenizer_class": "CamembertTokenizer",
|
78 |
+
"unk_token": "<unk>"
|
79 |
+
}
|