Create README.md

README.md

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+---
+pipeline_tag: text-classification
+language: fr
+license: mit
+datasets:
+- unicamp-dl/mmarco
+metrics:
+- recall
+tags:
+- passage-reranking
+library_name: sentence-transformers
+base_model: microsoft/mdeberta-v3-base
+model-index:
+- name: crossencoder-mdebertav3-base-mmarcoFR
+  results:
+    - task:
+        type: text-classification
+        name: Passage Reranking
+      dataset:
+        type: unicamp-dl/mmarco
+        name: mMARCO-fr
+        config: french
+        split: validation
+      metrics:
+        - type: recall_at_500
+          name: Recall@500
+          value: 97.40
+        - type: recall_at_100
+          name: Recall@100
+          value: 87.41
+        - type: recall_at_10
+          name: Recall@10
+          value: 62.52
+        - type: mrr_at_10
+          name: MRR@10
+          value: 36.16
+---
+
+# crossencoder-mdebertav3-base-mmarcoFR
+
+This is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. 
+The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage 
+retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of 
+relevance according to the model's predicted scores.
+
+## Usage
+
+Here are some examples for using the model with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), or [Huggingface Transformers](#using-huggingface-transformers).
+
+#### Using Sentence-Transformers
+
+Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. Then, you can use the model like this:
+
+```python
+from sentence_transformers import CrossEncoder
+
+pairs = [('Question', 'Paragraphe 1'), ('Question', 'Paragraphe 2') , ('Question', 'Paragraphe 3')]
+
+model = CrossEncoder('antoinelouis/crossencoder-mdebertav3-base-mmarcoFR')
+scores = model.predict(pairs)
+print(scores)
+```
+
+#### Using FlagEmbedding
+
+Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this:
+
+```python
+from FlagEmbedding import FlagReranker
+
+pairs = [('Question', 'Paragraphe 1'), ('Question', 'Paragraphe 2') , ('Question', 'Paragraphe 3')]
+
+reranker = FlagReranker('antoinelouis/crossencoder-mdebertav3-base-mmarcoFR')
+scores = reranker.compute_score(pairs)
+print(scores)
+```
+
+#### Using HuggingFace Transformers
+
+Start by installing the [library](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+pairs = [('Question', 'Paragraphe 1'), ('Question', 'Paragraphe 2') , ('Question', 'Paragraphe 3')]
+
+tokenizer = AutoTokenizer.from_pretrained('antoinelouis/crossencoder-mdebertav3-base-mmarcoFR')
+model = AutoModelForSequenceClassification.from_pretrained('antoinelouis/crossencoder-mdebertav3-base-mmarcoFR')
+model.eval()
+
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+print(scores)
+```
+
+***
+
+## Evaluation
+
+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 which 
+an ensemble of 1000 passages containing the positive(s) and [ColBERTv2 hard negatives](https://huggingface.co/datasets/antoinelouis/msmarco-dev-small-negatives) need 
+to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out 
+the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
+
+***
+
+## Training
+
+#### Data
+
+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
+that contains 8.8M passages and 539K training queries. We do not use the BM25 negatives provided by the official dataset but instead sample harder negatives mined from 
+12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives#msmarco-hard-negativesjsonlgz) 
+distillation dataset. Eventually, we sample 2.6M training triplets of the form (query, passage, relevance) with a positive-to-negative ratio of 1 (i.e., 50% of the pairs are 
+relevant and 50% are irrelevant).
+
+#### Implementation
+
+The model is initialized from the [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) checkpoint and optimized via the binary cross-entropy loss 
+(as in [monoBERT](https://doi.org/10.48550/arXiv.1910.14424)). It is fine-tuned on one 80GB NVIDIA H100 GPU for 20k steps using the AdamW optimizer 
+with a batch size of 128 and a constant learning rate of 2e-5. We set the maximum sequence length of the concatenated question-passage pairs to 256 tokens. 
+We use the sigmoid function to get scores between 0 and 1.
+
+***
+
+## Citation
+
+```bibtex
+@online{louis2024decouvrir,
+	author    = 'Antoine Louis',
+	title     = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French',
+	publisher = 'Hugging Face',
+	month     = 'mar',
+	year      = '2024',
+	url       = 'https://huggingface.co/spaces/antoinelouis/decouvrir',
+}
+```