Commit
•
50eafd8
1
Parent(s):
afe6568
Init: 모델 최초 commit
Browse files- README.md +102 -1
- all_results.json +20 -0
- config.json +56 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +5 -0
- trainer_state.json +1576 -0
- training_args.bin +3 -0
README.md
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---
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---
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---
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language:
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- ko # Example: fr
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license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
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library_name: transformers # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
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tags:
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- text2text-generation # Example: audio
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datasets:
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- aihub # Example: common_voice. Use dataset id from https://hf.co/datasets
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metrics:
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- bleu # Example: wer. Use metric id from https://hf.co/metrics
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- rouge
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# Optional. Add this if you want to encode your eval results in a structured way.
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model-index:
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- name: ko-TextNumbarT
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results:
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- task:
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type: text2text-generation # Required. Example: automatic-speech-recognition
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name: text2text-generation # Optional. Example: Speech Recognition
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metrics:
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- type: bleu # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9529006548919251 # Required. Example: 20.90
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name: eval_bleu # Optional. Example: Test WER
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verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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- type: rouge1 # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9693520563208838 # Required. Example: 20.90
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name: eval_rouge1 # Optional. Example: Test WER
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verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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- type: rouge2 # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9444220599246154 # Required. Example: 20.90
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name: eval_rouge2 # Optional. Example: Test WER
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verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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- type: rougeL # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9692485601662657 # Required. Example: 20.90
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name: eval_rougeL # Optional. Example: Test WER
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verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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- type: rougeLsum # Required. Example: wer. Use metric id from https://hf.co/metrics
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value: 0.9692422603343052 # Required. Example: 20.90
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name: eval_rougeLsum # Optional. Example: Test WER
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verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
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---
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# ko-TextNumbarT(TNT Model🧨): Try Korean Reading To Number(한글을 숫자로 바꾸는 모델)
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## Table of Contents
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- [ko-TextNumbarT(TNT Model🧨): Try Korean Reading To Number(한글을 숫자로 바꾸는 모델)](#ko-textnumbarttnt-model-try-korean-reading-to-number한글을-숫자로-바꾸는-모델)
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- [Table of Contents](#table-of-contents)
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- [Model Details](#model-details)
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- [Uses](#uses)
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- [Evaluation](#evaluation)
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- [How to Get Started With the Model](#how-to-get-started-with-the-model)
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## Model Details
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- **Model Description:**
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뭔가 찾아봐도 모델이나 알고리즘이 딱히 없어서 만들어본 모델입니다. <br />
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BartForConditionalGeneration Fine-Tuning Model For Korean To Number <br />
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BartForConditionalGeneration으로 파인튜닝한, 한글을 숫자로 변환하는 Task 입니다. <br />
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- Dataset use [Korea aihub](https://aihub.or.kr/aihubdata/data/list.do?currMenu=115&topMenu=100&srchDataRealmCode=REALM002&srchDataTy=DATA004) <br />
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I can't open my fine-tuning datasets for my private issue <br />
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데이터셋은 Korea aihub에서 받아서 사용하였으며, 파인튜닝에 사용된 모든 데이터를 사정상 공개해드릴 수는 없습니다. <br />
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- Korea aihub data is ONLY permit to Korean!!!!!!! <br />
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aihub에서 데이터를 받으실 분은 한국인일 것이므로, 한글로만 작성합니다. <br />
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정확히는 철자전사를 음성전사로 번역하는 형태로 학습된 모델입니다. (ETRI 전사기준) <br />
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- In case, ten million, some people use 10 million or some people use 10000000, so this model is crucial for training datasets
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천만을 1000만 혹은 10000000으로 쓸 수도 있기에, Training Datasets에 따라 결과는 상이할 수 있습니다. <br />
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- **Developed by:** Yoo SungHyun(https://github.com/YooSungHyun)
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- **Language(s):** Korean
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- **License:** apache-2.0
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- **Parent Model:** See the [kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) for more information about the pre-trained base model.
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## Uses
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Want see more detail follow this URL [KoGPT_num_converter](https://github.com/ddobokki/KoGPT_num_converter) <br /> and see `bart_inference.py` and `bart_train.py`
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## Evaluation
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Just using `evaluate-metric/bleu` and `evaluate-metric/rouge` in huggingface `evaluate` library <br />
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[Training wanDB URL](https://wandb.ai/bart_tadev/BartForConditionalGeneration/runs/1chrc03q?workspace=user-bart_tadev)
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## How to Get Started With the Model
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```python
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from transformers.pipelines import Text2TextGenerationPipeline
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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texts = ["그러게 누가 여섯시까지 술을 마시래?"]
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tokenizer = AutoTokenizer.from_pretrained(
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args.model_name_or_path,
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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args.model_name_or_path,
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)
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seq2seqlm_pipeline = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
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kwargs = {
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"min_length": args.min_length,
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"max_length": args.max_length,
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"num_beams": args.beam_width,
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"do_sample": args.do_sample,
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"num_beam_groups": args.num_beam_groups,
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}
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pred = seq2seqlm_pipeline(texts, **kwargs)
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print(pred)
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# 그러게 누가 6시까지 술을 마시래?
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```
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all_results.json
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{
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"epoch": 5.13,
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"eval_bleu": 0.9529006548919251,
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"eval_brevity_penalty": 1.0,
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"eval_length_ratio": 1.0129792088807863,
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"eval_loss": 0.040760207921266556,
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"eval_reference_length": 688948,
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"eval_rouge1": 0.9693520563208838,
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"eval_rouge2": 0.9444220599246154,
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"eval_rougeL": 0.9692485601662657,
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"eval_rougeLsum": 0.9692422603343052,
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"eval_runtime": 348.5598,
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"eval_samples_per_second": 122.995,
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"eval_steps_per_second": 10.251,
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"eval_translation_length": 697890,
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"train_loss": 0.08529333166642622,
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"train_runtime": 78966.8059,
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"train_samples_per_second": 25.074,
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"train_steps_per_second": 4.179
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}
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config.json
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{
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"_name_or_path": "/data2/bart/temp_workspace/nlp/models/kobart-base-v2",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"add_bias_logits": false,
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"add_final_layer_norm": false,
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"architectures": [
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"BartForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"author": "Heewon Jeon(madjakarta@gmail.com)",
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"bos_token_id": 1,
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"classif_dropout": 0.1,
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"classifier_dropout": 0.1,
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"d_model": 768,
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"decoder_attention_heads": 16,
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"decoder_ffn_dim": 3072,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"decoder_start_token_id": 1,
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"do_blenderbot_90_layernorm": false,
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"dropout": 0.1,
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"encoder_attention_heads": 16,
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"encoder_ffn_dim": 3072,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_token_id": 1,
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"extra_pos_embeddings": 2,
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"force_bos_token_to_be_generated": false,
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"forced_eos_token_id": 1,
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"gradient_checkpointing": false,
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"id2label": {
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"0": "NEGATIVE",
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"1": "POSITIVE"
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},
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"kobart_version": 2.0,
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"label2id": {
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"NEGATIVE": 0,
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"POSITIVE": 1
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},
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"max_position_embeddings": 1026,
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"model_type": "bart",
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"normalize_before": false,
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"normalize_embedding": true,
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"num_hidden_layers": 6,
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"pad_token_id": 3,
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"scale_embedding": false,
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"static_position_embeddings": false,
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"tokenizer_class": "PreTrainedTokenizerFast",
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"torch_dtype": "float32",
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"transformers_version": "4.22.1",
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"use_cache": true,
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"vocab_size": 30000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:002675439d3f8ffec4c9410dcb0992e50f1db850b5e49a2e207ec30b08c86997
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size 495646265
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special_tokens_map.json
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{
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"bos_token": "</s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"name_or_path": "/data2/bart/temp_workspace/nlp/models/kobart-base-v2",
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"special_tokens_map_file": "/data2/bart/temp_workspace/nlp/models/kobart-base-v2/special_tokens_map.json",
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
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trainer_state.json
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