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Browse files- ChatLM-mini-Chinese/.gitattributes +35 -0
- ChatLM-mini-Chinese/README.md +0 -0
- ChatLM-mini-Chinese/config.json +33 -0
- ChatLM-mini-Chinese/configuration_chat_model.py +4 -0
- ChatLM-mini-Chinese/generation_config.json +7 -0
- ChatLM-mini-Chinese/model.safetensors +3 -0
- ChatLM-mini-Chinese/modeling_chat_model.py +74 -0
- ChatLM-mini-Chinese/special_tokens_map.json +5 -0
- ChatLM-mini-Chinese/tokenizer.json +0 -0
- ChatLM-mini-Chinese/tokenizer_config.json +66 -0
ChatLM-mini-Chinese/.gitattributes
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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ChatLM-mini-Chinese/README.md
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ChatLM-mini-Chinese/config.json
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{
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"_name_or_path": "./model_save/dpo/",
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"architectures": [
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"TextToTextModel"
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],
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"auto_map": {
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"AutoModelForSeq2SeqLM": "modeling_chat_model.TextToTextModel"
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},
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"classifier_dropout": 0.0,
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": false,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"num_decoder_layers": 10,
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"num_heads": 12,
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"num_layers": 10,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.36.2",
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"use_cache": true,
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"vocab_size": 29298
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}
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ChatLM-mini-Chinese/configuration_chat_model.py
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from transformers import T5Config
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class TextToTextModelConfig(T5Config):
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model_type = 't5'
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ChatLM-mini-Chinese/generation_config.json
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{
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"_from_model_config": true,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.36.2"
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}
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ChatLM-mini-Chinese/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:054caeae92bcc13f0b6e7a12f86e75c8e18117279ecd89c4aa1f8ac74c95c02a
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size 750794624
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ChatLM-mini-Chinese/modeling_chat_model.py
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import torch
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from torch import Tensor, LongTensor
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from transformers import T5ForConditionalGeneration, T5Config
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from transformers import TextIteratorStreamer
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from transformers.generation.configuration_utils import GenerationConfig
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class TextToTextModel(T5ForConditionalGeneration):
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def __init__(self, config: T5Config) -> None:
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'''
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TextToTextModel继承T5ForConditionalGeneration
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'''
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super().__init__(config)
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@torch.no_grad()
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def my_generate(self,
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input_ids: LongTensor,
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attention_mask: LongTensor,
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max_seq_len: int=256,
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search_type: str='beam',
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streamer: TextIteratorStreamer=None,
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) -> Tensor:
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'''
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自定义gennerate方法方便调用、测试
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search_type: ['greedy', 'beam', 'sampling', 'contrastive', ]
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- *greedy decoding* by calling [`~generation.GenerationMixin.greedy_search`] if `num_beams=1` and
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`do_sample=False`
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- *contrastive search* by calling [`~generation.GenerationMixin.contrastive_search`] if `penalty_alpha>0.`
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and `top_k>1`
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- *multinomial sampling* by calling [`~generation.GenerationMixin.sample`] if `num_beams=1` and
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`do_sample=True`
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- *beam-search decoding* by calling [`~generation.GenerationMixin.beam_search`] if `num_beams>1` and
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`do_sample=False`
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- *beam-search multinomial sampling* by calling [`~generation.GenerationMixin.beam_sample`] if
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`num_beams>1` and `do_sample=True`
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'''
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generation_config = GenerationConfig()
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generation_config.remove_invalid_values = True
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generation_config.eos_token_id = 1
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generation_config.pad_token_id = 0
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generation_config.decoder_start_token_id = self.config.decoder_start_token_id
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generation_config.max_new_tokens = max_seq_len
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# generation_config.repetition_penalty = 1.1 # 重复词惩罚
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if search_type == 'greedy':
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generation_config.num_beams = 1
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generation_config.do_sample = False
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elif search_type == 'beam':
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generation_config.top_k = 50
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generation_config.num_beams = 5
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generation_config.do_sample = True
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generation_config.top_p = 0.95
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generation_config.no_repeat_ngram_size = 4
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generation_config.length_penalty = -2.0
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generation_config.early_stopping = True
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elif search_type == 'sampling':
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generation_config.num_beams = 1
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generation_config.do_sample = True
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generation_config.top_k = 50
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generation_config.temperature = 0.98 # 越低概率越趋向于均匀分布
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generation_config.top_p = 0.80
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generation_config.no_repeat_ngram_size = 4
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elif search_type == 'contrastive':
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generation_config.penalty_alpha = 0.5
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generation_config.top_k = 50
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result = self.generate(
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inputs=input_ids,
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attention_mask=attention_mask,
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generation_config=generation_config,
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streamer=streamer,
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)
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return result
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ChatLM-mini-Chinese/special_tokens_map.json
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{
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"eos_token": "[EOS]",
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"pad_token": "[PAD]",
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"unk_token": "[UNK]"
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}
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ChatLM-mini-Chinese/tokenizer.json
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ChatLM-mini-Chinese/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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"1": {
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"content": "[EOS]",
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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": "[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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"3": {
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"content": "[BOS]",
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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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"4": {
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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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"5": {
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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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"6": {
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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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},
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"clean_up_tokenization_spaces": true,
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"eos_token": "[EOS]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "[UNK]"
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}
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