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. Here are some examples for using the model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers. The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for which an ensemble of 1000 passages containing the positive(s) and ColBERTv2 hard 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* leaderboard.
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View details ↗--- pipeline_tag: sentence-similarity language: fr license: mit tags: - passage-retrieval - sentence-similarity - pruned library_name: sentence-transformers base_model: BAAI/bge-m3 base_model_relation: quantized --- This model is a 38.4% smaller version of BAAI/bge-m3 for the French language, created using the mtem-pruner space. This pruned model should perform similarly to the original model for French language tasks with a much smaller memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not commonly used in French were removed from the original multilingual model's vocabulary.
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View details ↗This is a SPLADE-max model for **French** that can be used for semantic search. The model maps queries and passages to 32k-dimensional sparse vectors which are used to compute relevance through cosine similarity. Start by installing the library: `pip install -U transformers`. Then, you can use the model like this: The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for a corpus of 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). To see how it compares to other neural retrievers in French, check out the *DécouvrIR* leaderboard.
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View details ↗Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
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View details ↗Fine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the Mongolian test data of Common Voice.
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View details ↗Fine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the Slovenian test data of Common Voice.
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View details ↗Converted files of the original model at mamba2-1.3b to HF transformers compatible formats. Not affiliated with both the original authors or hf.
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View details ↗Converted files of the original model at mamba2-130m to HF transformers compatible formats. Not affiliated with both the original authors or hf.
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View details ↗Converted files of the original model at mamba2-2.7b to HF transformers compatible formats. Not affiliated with both the original authors or hf.
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