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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-similarity
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+ library_name: sentence-transformers
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+ ---
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+ # crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the **French** portion of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
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+
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+ It performs cross-attention between a question-passage pair and outputs a relevance score between 0 and 1. The model can be used for tasks like clustering or [semantic search]((https://www.sbert.net/examples/applications/retrieve_rerank/README.html): given a query, encode the latter with some candidate passages -- e.g., retrieved with BM25 or a biencoder -- then sort the passages in a decreasing order of relevance according to the model's predictions.
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+
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+ ## Usage
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+ ***
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+
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+ #### Sentence-Transformers
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+
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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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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import CrossEncoder
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+ pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]
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+
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+ model = CrossEncoder('crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR')
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+ scores = model.predict(pairs)
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+ print(scores)
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+ ```
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+
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+ #### 🤗 Transformers
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+
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+ Without [sentence-transformers](https://www.SBERT.net), you can use the model as follows:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+
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+ model = AutoModelForSequenceClassification.from_pretrained('crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR')
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+ tokenizer = AutoTokenizer.from_pretrained('crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR')
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+
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+ pairs = [('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')]
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+ features = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
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+
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+ model.eval()
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+ with torch.no_grad():
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+ scores = model(**features).logits
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+ print(scores)
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+ ```
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+
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+ ## Evaluation
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+ ***
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+
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+ We evaluated our model on 500 random queries from the mMARCO-fr train set (which were excluded from training). Each of these queries has at least one relevant and up to 200 irrelevant passages.
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+
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+ | r-precision | mrr@10 | recall@10 | recall@20 | recall@50 | recall@100 |
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+ |--------------:|---------:|------------:|------------:|------------:|-------------:|
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+ | 34.37 | 51.01 | 82.23 | 90.6 | 96.45 | 98.4 |
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+
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+ Below, we compared its results with other cross-encoder models fine-tuned on the same dataset:
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+ | | model | r-precision | mrr@10 | recall@10 (↑) | recall@20 | recall@50 | recall@100 |
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+ |---:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------:|---------:|------------:|------------:|------------:|-------------:|
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+ | 1 | [crossencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR) | 35.65 | 50.44 | 82.95 | 91.5 | 96.8 | 98.8 |
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+ | 2 | **crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR** | 34.37 | 51.01 | 82.23 | 90.6 | 96.45 | 98.4 |
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+ | 3 | [crossencoder-mmarcoFR-mMiniLMv2-L12-H384-v1-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mmarcoFR-mMiniLMv2-L12-H384-v1-mmarcoFR) | 34.22 | 49.2 | 81.7 | 90.9 | 97.1 | 98.9 |
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+ | 4 | [crossencoder-mpnet-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mpnet-base-mmarcoFR) | 29.68 | 46.13 | 80.45 | 87.9 | 93.15 | 96.6 |
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+ | 5 | [crossencoder-distilcamembert-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-base-mmarcoFR) | 27.28 | 43.71 | 80.3 | 89.1 | 95.55 | 98.6 |
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+ | 6 | [crossencoder-roberta-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-roberta-base-mmarcoFR) | 33.33 | 48.87 | 79.33 | 86.75 | 94.15 | 97.6 |
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+ | 7 | [crossencoder-electra-base-french-europeana-cased-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-electra-base-french-europeana-cased-discriminator-mmarcoFR) | 28.32 | 45.28 | 79.22 | 87.15 | 93.15 | 95.75 |
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+ | 8 | [crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L6-H384-distilled-from-XLMR-Large-mmarcoFR) | 33.92 | 49.33 | 79 | 88.35 | 94.8 | 98.2 |
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+ | 9 | [crossencoder-msmarco-electra-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-electra-base-mmarcoFR) | 25.52 | 42.46 | 78.73 | 88.85 | 96.55 | 98.85 |
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+ | 10 | [crossencoder-bert-base-uncased-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-bert-base-uncased-mmarcoFR) | 30.48 | 45.79 | 78.35 | 89.45 | 94.15 | 97.45 |
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+ | 11 | [crossencoder-msmarco-MiniLM-L-12-v2-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-MiniLM-L-12-v2-mmarcoFR) | 29.07 | 44.41 | 77.83 | 88.1 | 95.55 | 99 |
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+ | 12 | [crossencoder-msmarco-MiniLM-L-6-v2-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-MiniLM-L-6-v2-mmarcoFR) | 32.92 | 47.56 | 77.27 | 88.15 | 94.85 | 98.15 |
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+ | 13 | [crossencoder-msmarco-MiniLM-L-4-v2-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-MiniLM-L-4-v2-mmarcoFR) | 30.98 | 46.22 | 76.35 | 85.8 | 94.35 | 97.55 |
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+ | 14 | [crossencoder-MiniLM-L6-H384-uncased-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-MiniLM-L6-H384-uncased-mmarcoFR) | 29.23 | 45.12 | 76.08 | 83.7 | 92.65 | 97.45 |
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+ | 15 | [crossencoder-electra-base-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-electra-base-discriminator-mmarcoFR) | 28.48 | 43.58 | 75.63 | 86.15 | 93.25 | 96.6 |
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+ | 16 | [crossencoder-electra-small-discriminator-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-electra-small-discriminator-mmarcoFR) | 31.83 | 45.97 | 75.13 | 84.95 | 94.55 | 98.15 |
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+ | 17 | [crossencoder-distilroberta-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-distilroberta-base-mmarcoFR) | 28.22 | 42.85 | 74.13 | 84.08 | 94.2 | 98.5 |
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+ | 18 | [crossencoder-msmarco-TinyBERT-L-6-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-TinyBERT-L-6-mmarcoFR) | 28.23 | 42.7 | 73.63 | 85.65 | 92.65 | 98.35 |
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+ | 19 | [crossencoder-msmarco-TinyBERT-L-4-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-TinyBERT-L-4-mmarcoFR) | 28.6 | 43.19 | 72.17 | 81.95 | 92.8 | 97.4 |
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+ | 20 | [crossencoder-msmarco-MiniLM-L-2-v2-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-MiniLM-L-2-v2-mmarcoFR) | 30.82 | 44.3 | 72.03 | 82.65 | 93.35 | 98.1 |
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+ | 21 | [crossencoder-distilbert-base-uncased-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-distilbert-base-uncased-mmarcoFR) | 25.47 | 40.11 | 71.37 | 85.6 | 93.85 | 97.95 |
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+ | 22 | [crossencoder-msmarco-TinyBERT-L-2-v2-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-msmarco-TinyBERT-L-2-v2-mmarcoFR) | 31.08 | 43.88 | 71.3 | 81.43 | 92.6 | 98.1 |
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+
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+ ## Training
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+ ***
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+
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+ #### Background
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+
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+ We used the [nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model and fine-tuned it with a binary cross-entropy loss function on 1M question-passage pairs in French with a positive-to-negative ratio of 4 (i.e., 25% of the pairs are relevant and 75% are irrelevant).
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+
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+ #### Hyperparameters
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+
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+ We trained the model on a single Tesla V100 GPU with 32GBs of memory during 10 epochs (i.e., 312.4k steps) using a batch size of 32. We used the adamw optimizer with an initial learning rate of 2e-05, weight decay of 0.01, learning rate warmup over the first 500 steps, and linear decay of the learning rate. The sequence length was limited to 512 tokens.
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+
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+ #### Data
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+
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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 popular large-scale IR dataset.
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+
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+ ## Citation
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+ ***
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+
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+ ```bibtex
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+ @online{louis2023,
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+ author = 'Antoine Louis',
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+ title = 'crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR: A Cross-Encoder Model Trained on 1M sentence pairs in French',
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+ publisher = 'Hugging Face',
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+ month = 'september',
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+ year = '2023',
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+ url = 'https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-H384-distilled-from-XLMR-Large-mmarcoFR',
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+ }
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+ ```
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