antoinelouis
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Upload pruned model
Browse files- 1_Pooling/config.json +7 -0
- README.md +43 -0
- config.json +50 -0
- model.safetensors +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +54 -0
1_Pooling/config.json
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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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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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README.md
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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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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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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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## Usage
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You can use this model with the Transformers library:
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```python
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from transformers import AutoModel, AutoTokenizer
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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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Or with the sentence-transformers library:
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("antoinelouis/french-gte-multilingual-base")
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```
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**Credits**: cc [@antoinelouis](https://huggingface.co/antoinelouis)
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config.json
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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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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"LABEL_0": 0
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},
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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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"factor": 8.0,
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"type": "ntk"
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},
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"rope_theta": 20000,
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"torch_dtype": "float32",
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"transformers_version": "4.45.1",
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"type_vocab_size": 1,
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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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5b34f6f8e4686505d2a44fd944bbd38ace6b922cdde73a43e2088ad6a9985c81
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size 567618688
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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sentence_bert_config.json
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{
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"max_seq_length": 8192,
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"do_lower_case": false
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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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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},
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"cls_token": {
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"content": "<s>",
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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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},
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"eos_token": {
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"content": "</s>",
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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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},
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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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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},
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"sep_token": {
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"content": "</s>",
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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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},
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"unk_token": {
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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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}
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}
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tokenizer.json
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See raw diff
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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": "<s>",
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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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"1": {
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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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"2": {
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"content": "</s>",
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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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"3": {
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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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"37199": {
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"content": "<mask>",
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"lstrip": true,
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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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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"model_max_length": 32768,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "<unk>"
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}
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