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--- |
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pipeline_tag: sentence-similarity |
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language: fr |
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license: mit |
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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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- passage-retrieval |
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library_name: sentence-transformers |
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base_model: antoinelouis/camembert-L2 |
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model-index: |
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- name: biencoder-camembert-L2-mmarcoFR |
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results: |
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- task: |
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type: sentence-similarity |
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name: Passage Retrieval |
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dataset: |
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type: unicamp-dl/mmarco |
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name: mMARCO-fr |
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config: french |
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split: validation |
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metrics: |
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- type: recall_at_500 |
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name: Recall@500 |
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value: 81.0 |
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- type: recall_at_100 |
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name: Recall@100 |
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value: 66.3 |
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- type: recall_at_10 |
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name: Recall@10 |
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value: 38.5 |
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- type: mrr_at_10 |
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name: MRR@10 |
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value: 20.1 |
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- type: ndcg_at_10 |
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name: nDCG@10 |
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value: 24.3 |
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- type: map_at_10 |
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name: MAP@10 |
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value: 19.7 |
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--- |
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# biencoder-camembert-L2-mmarcoFR |
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This is a lightweight dense single-vector bi-encoder model for **French** that can be used for semantic search. |
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The model maps queries and passages to 768-dimensional dense vectors which are used to compute relevance through cosine similarity. |
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It uses a [CamemBERT-L2](https://huggingface.co./antoinelouis/camembert-L2) backbone, which is a pruned version of the pre-trained [CamemBERT](https://huggingface.co./camembert-base) |
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checkpoint with 64% less parameters, obtained by [dropping the top-layers](https://doi.org/10.48550/arXiv.2004.03844) from the original model. |
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## Usage |
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Here are some examples for using this model with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), or [Huggingface Transformers](#using-huggingface-transformers). |
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#### Using Sentence-Transformers |
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Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. 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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queries = ["Ceci est un exemple de requête.", "Voici un second exemple."] |
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passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."] |
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model = SentenceTransformer('antoinelouis/biencoder-camembert-L2-mmarcoFR') |
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q_embeddings = model.encode(queries, normalize_embeddings=True) |
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p_embeddings = model.encode(passages, normalize_embeddings=True) |
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similarity = q_embeddings @ p_embeddings.T |
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print(similarity) |
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``` |
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#### Using FlagEmbedding |
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Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this: |
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```python |
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from FlagEmbedding import FlagModel |
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queries = ["Ceci est un exemple de requête.", "Voici un second exemple."] |
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passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."] |
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model = FlagModel('antoinelouis/biencoder-camembert-L2-mmarcoFR') |
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q_embeddings = model.encode(queries, normalize_embeddings=True) |
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p_embeddings = model.encode(passages, normalize_embeddings=True) |
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similarity = q_embeddings @ p_embeddings.T |
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print(similarity) |
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``` |
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#### Using Transformers |
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Start by installing the [library](https://huggingface.co./docs/transformers): `pip install -U transformers`. Then, you can use the model like this: |
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```python |
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import torch |
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from torch.nn.functional import normalize |
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from transformers import AutoTokenizer, AutoModel |
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def mean_pooling(model_output, attention_mask): |
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""" Perform mean pooling on-top of the contextualized word embeddings, while ignoring mask tokens in the mean computation.""" |
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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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queries = ["Ceci est un exemple de requête.", "Voici un second exemple."] |
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passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."] |
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tokenizer = AutoTokenizer.from_pretrained('antoinelouis/biencoder-camembert-L2-mmarcoFR') |
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model = AutoModel.from_pretrained('antoinelouis/biencoder-camembert-L2-mmarcoFR') |
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q_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt') |
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p_input = tokenizer(passages, padding=True, truncation=True, return_tensors='pt') |
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with torch.no_grad(): |
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q_output = model(**encoded_queries) |
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p_output = model(**encoded_passages) |
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q_embeddings = mean_pooling(q_output, q_input['attention_mask']) |
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q_embedddings = normalize(q_embeddings, p=2, dim=1) |
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p_embeddings = mean_pooling(p_output, p_input['attention_mask']) |
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p_embedddings = normalize(p_embeddings, p=2, dim=1) |
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similarity = q_embeddings @ p_embeddings.T |
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print(similarity) |
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``` |
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## Evaluation |
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The model is evaluated on the smaller development set of [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/), which consists of 6,980 queries for a corpus of |
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8.8M candidate passages. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k). |
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To see how it compares to other neural retrievers in French, check out the [*DécouvrIR*](https://huggingface.co./spaces/antoinelouis/decouvrir) leaderboard. |
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## Training |
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#### Data |
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We use the French training samples from the [mMARCO](https://huggingface.co./datasets/unicamp-dl/mmarco) dataset, a multilingual machine-translated version of MS MARCO |
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that contains 8.8M passages and 539K training queries. We do not employ the BM25 negatives provided by the official dataset but instead sample harder negatives mined |
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from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co./datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset. |
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#### Implementation |
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The model is initialized from the [camembert-L2](https://huggingface.co./antoinelouis/camembert-L2) checkpoint and optimized via the cross-entropy loss |
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(as in [DPR](https://doi.org/10.48550/arXiv.2004.04906)) with a temperature of 0.05. It is fine-tuned on one 32GB NVIDIA V100 GPU for 9.8k steps (or 40 epochs) |
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using the AdamW optimizer with a batch size of 2048, a peak learning rate of 2e-5 with warm up along the first 976 steps and linear scheduling. |
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We set the maximum sequence lengths for both the questions and passages to 128 tokens. We use the cosine similarity to compute relevance scores. |
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## Citation |
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```bibtex |
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@online{louis2024decouvrir, |
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author = 'Antoine Louis', |
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title = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French', |
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publisher = 'Hugging Face', |
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month = 'mar', |
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year = '2024', |
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url = 'https://huggingface.co./spaces/antoinelouis/decouvrir', |
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} |
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``` |
