Commit
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04a2abe
1
Parent(s):
71867c1
feat: push custom model
Browse files- 1_Pooling/config.json +10 -0
- README.md +24 -0
- config.json +35 -0
- config_sentence_transformers.json +9 -0
- model.safetensors +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- training_args.bin +3 -0
- vocab.txt +0 -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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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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README.md
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# complete_9062874564
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## Model Description
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complete_9062874564 is a state-of-the-art embedding model designed to support various applications in natural language processing and understanding. It's built using the latest advancements in deep learning and natural language processing technologies to provide high-quality embeddings that capture contextual nuances and semantic meanings.
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## Use Cases
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This model is designed to support various applications in natural language processing and understanding.
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## How to Use
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This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
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```python
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from transformers import AutoModel, AutoTokenizer
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model_name = "complete_9062874564"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModel.from_pretrained(model_name)
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tokens = tokenizer("Your text here", return_tensors="pt")
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embedding = model(**tokens)
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```
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config.json
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{
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"_name_or_path": "mytmp/finetuned_model",
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"architectures": [
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"BertModel"
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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": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
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"AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
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"AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
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"AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
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},
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"classifier_dropout": null,
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"emb_pooler": "mean",
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"feed_forward_type": "geglu",
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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": 8192,
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"model_max_length": 8192,
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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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"pad_token_id": 0,
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"position_embedding_type": "alibi",
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"torch_dtype": "float32",
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"transformers_version": "4.39.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30528
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.6.1",
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"transformers": "4.39.3",
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"pytorch": "2.2.2+cu121"
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},
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"prompts": {},
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"default_prompt_name": null
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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:f1e62e762c321235067cd6eedf37dd11a8988e71e8c1b7d3fdbae595027bbcbb
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size 461562736
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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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"cls_token": {
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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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},
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"mask_token": {
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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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},
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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": "[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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},
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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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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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"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": 2147483648,
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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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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:61a3e6dd60c42247cd786fb36302da7e31c2f3278b3a5f94629d2f04901b333b
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size 5112
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vocab.txt
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