Raphael Sourty
commited on
upload model
Browse files- README.md +61 -0
- config.json +27 -0
- generation_config.json +5 -0
- linear.pt +3 -0
- metadata.json +10 -0
- model.safetensors +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +58 -0
- vocab.txt +0 -0
README.md
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---
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license: mit
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---
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---
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language:
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- en
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license: mit
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---
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This model was trained with [Neural-Cherche](https://github.com/raphaelsty/neural-cherche). You can find details on how to fine-tune it in the [Neural-Cherche](https://github.com/raphaelsty/neural-cherche) repository.
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```sh
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pip install neural-cherche
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```
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## Retriever
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```python
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from neural_cherche import models, retrieve
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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batch_size = 32
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documents = [
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{"id": 0, "document": "Food"},
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{"id": 1, "document": "Sports"},
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{"id": 2, "document": "Cinema"},
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]
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queries = ["Food", "Sports", "Cinema"]
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model = SparseEmbed(
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model_name_or_path="raphaelsty/neural-cherche-sparse-embed",
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device=device,
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)
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retriever = retrieve.SparseEmbed(
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key="id",
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on=["document"],
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model=model,
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)
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documents_embeddings = retriever.encode_documents(
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documents=documents,
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batch_size=batch_size,
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)
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retriever = retriever.add(
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documents_embeddings=documents_embeddings,
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)
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queries_embeddings = retriever.encode_queries(
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queries=queries,
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batch_size=batch_size,
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)
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scores = retriever(
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queries_embeddings=queries_embeddings,
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batch_size=batch_size,
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k=100,
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k_ranker=10,
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)
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scores
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```
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config.json
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{
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"_name_or_path": "raphaelsty/neural-cherche-sparse-embed",
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_hidden_states": true,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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generation_config.json
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{
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"_from_model_config": true,
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"pad_token_id": 0,
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"transformers_version": "4.35.2"
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}
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linear.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:2cd1265d36bedccc9ef3e4329d46510010a908257867a21840e46ec8c3a45f9f
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size 394391
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metadata.json
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{
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"max_length_query": 64,
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"max_length_document": 256,
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"query_prefix": "",
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"document_prefix": "",
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"padding": "max_length",
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"truncation": true,
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"add_special_tokens": true,
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"n_mask_tokens": 3
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f28d2cd97f79d1a6d5f6ef3a4f2e5e022b9c293dd982e29c7cc21ef9dfe9bbfd
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size 438080896
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"device": "cuda",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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