antoinelouis commited on
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Upload pruned model

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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ }
README.md ADDED
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+
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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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+ tags:
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+ - passage-retrieval
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+ - sentence-similarity
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+ - pruned
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+ library_name: sentence-transformers
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+ base_model: Alibaba-NLP/gte-multilingual-base
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+ base_model_relation: quantized
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+ ---
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+ # 🇫🇷 french-gte-multilingual-base
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+
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+ This model is a 53.5% smaller version of [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base)
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+ for the French language, created using the [mtem-pruner](https://huggingface.co/spaces/antoinelouis/mtem-pruner) space.
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+
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+ This pruned model should perform similarly to the original model for French language tasks with a much smaller
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+ memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not
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+ commonly used in French were removed from the original multilingual model's vocabulary.
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+
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+ ## Usage
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+
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+ You can use this model with the Transformers library:
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+
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+ ```python
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+ from transformers import AutoModel, AutoTokenizer
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+
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+ model_name = "antoinelouis/french-gte-multilingual-base"
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+ model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=True)
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+ ```
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+
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+ Or with the sentence-transformers library:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ model = SentenceTransformer("antoinelouis/french-gte-multilingual-base")
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+ ```
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+
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+ **Credits**: cc [@antoinelouis](https://huggingface.co/antoinelouis)
config.json ADDED
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+ {
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+ "_name_or_path": "Alibaba-NLP/gte-multilingual-base",
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+ "architectures": [
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+ "NewModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig",
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+ "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel",
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+ "AutoModelForMaskedLM": "Alibaba-NLP/new-impl--modeling.NewForMaskedLM",
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+ "AutoModelForMultipleChoice": "Alibaba-NLP/new-impl--modeling.NewForMultipleChoice",
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+ "AutoModelForQuestionAnswering": "Alibaba-NLP/new-impl--modeling.NewForQuestionAnswering",
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+ "AutoModelForSequenceClassification": "Alibaba-NLP/new-impl--modeling.NewForSequenceClassification",
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+ "AutoModelForTokenClassification": "Alibaba-NLP/new-impl--modeling.NewForTokenClassification"
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+ },
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+ "classifier_dropout": 0.0,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0"
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "layer_norm_type": "layer_norm",
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+ "logn_attention_clip1": false,
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+ "logn_attention_scale": false,
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+ "max_position_embeddings": 8192,
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+ "model_type": "new",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pack_qkv": true,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "rope",
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+ "rope_scaling": {
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+ "type": "ntk"
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+ },
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+ "rope_theta": 20000,
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+ "unpad_inputs": false,
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+ "use_memory_efficient_attention": false,
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+ "vocab_size": 37200
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+ }
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sentence_bert_config.json ADDED
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tokenizer.json ADDED
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tokenizer_config.json ADDED
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