Initial release
Browse files- README.md +102 -1
- all_results.json +14 -0
- config.json +37 -0
- eval_results.json +9 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer_config.json +19 -0
- train_results.json +8 -0
- trainer_state.json +646 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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---
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---
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language:
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- ja
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license: cc-by-sa-4.0
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tags:
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- zero-shot-classification
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- text-classification
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- nli
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- pytorch
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metrics:
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- accuracy
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datasets:
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- JSNLI
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pipeline_tag: text-classification
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widget:
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- text: "あなたが好きです。 あなたを愛しています。"
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model-index:
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- name: bert-base-japanese-jsnli
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results:
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- task:
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type: text-classification
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name: Natural Language Inference
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dataset:
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type: snli
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name: JSNLI
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split: dev
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metrics:
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- type: accuracy
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value: 0.9288
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verified: false
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---
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# bert-base-japanese-jsnli
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This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) on the [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2085
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- Accuracy: 0.9288
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### How to use the model
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#### Simple zero-shot classification pipeline
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```python
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from transformers import pipeline
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classifier = pipeline("zero-shot-classification", model="Formzu/bert-base-japanese-jsnli")
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sequence_to_classify = "いつか世界を見る。"
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candidate_labels = ['旅行', '料理', '踊り']
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out = classifier(sequence_to_classify, candidate_labels, hypothesis_template="この例は{}です。")
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print(out)
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#{'sequence': 'いつか世界を見る。',
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# 'labels': ['旅行', '料理', '踊り'],
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# 'scores': [0.6758995652198792, 0.22110949456691742, 0.1029909998178482]}
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```
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#### NLI use-case
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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model_name = "Formzu/bert-base-japanese-jsnli"
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model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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premise = "いつか世界を見る。"
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label = '旅行'
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hypothesis = f'この例は{label}です。'
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input = tokenizer.encode(premise, hypothesis, return_tensors='pt').to(device)
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with torch.no_grad():
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logits = model(input)["logits"][0]
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probs = logits.softmax(dim=-1)
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print(probs.cpu().numpy(), logits.cpu().numpy())
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#[0.68940836 0.29482093 0.01577068] [ 1.7791482 0.92968255 -1.998533 ]
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```
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| :-----------: | :---: | :---: | :-------------: | :------: |
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| 0.4054 | 1.0 | 16657 | 0.2141 | 0.9216 |
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| 0.3297 | 2.0 | 33314 | 0.2145 | 0.9236 |
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| 0.2645 | 3.0 | 49971 | 0.2085 | 0.9288 |
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### Framework versions
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- Transformers 4.21.2
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- Pytorch 1.12.1+cu116
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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all_results.json
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{
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"epoch": 3.0,
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"eval_accuracy": 0.9287538528442383,
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"eval_loss": 0.20848523080348969,
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"eval_runtime": 28.236,
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"eval_samples": 3916,
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"eval_samples_per_second": 138.688,
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"eval_steps_per_second": 4.356,
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"train_loss": 0.35883062176062114,
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"train_runtime": 31863.0731,
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"train_samples": 533005,
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"train_samples_per_second": 50.184,
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"train_steps_per_second": 1.568
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}
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config.json
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{
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"_name_or_path": "cl-tohoku/bert-base-japanese-v2",
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"architectures": [
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"BertForSequenceClassification"
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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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"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": "entailment",
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"1": "neutral",
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"2": "contradiction"
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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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"entailment": 0,
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"neutral": 1,
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"contradiction": 2
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},
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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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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"tokenizer_class": "BertJapaneseTokenizer",
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"torch_dtype": "float32",
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"transformers_version": "4.21.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 32768
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}
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eval_results.json
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{
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"epoch": 3.0,
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"eval_accuracy": 0.9287538528442383,
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| 4 |
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"eval_loss": 0.20848523080348969,
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| 5 |
+
"eval_runtime": 28.236,
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| 6 |
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"eval_samples": 3916,
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"eval_samples_per_second": 138.688,
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"eval_steps_per_second": 4.356
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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:e82604c4b2847e5a709153fd231dfc89be1a4ec5fa5cfe18f104731ddbd4b582
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size 444908909
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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_config.json
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{
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"do_subword_tokenize": true,
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"do_word_tokenize": true,
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"mask_token": "[MASK]",
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"mecab_kwargs": {
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"mecab_dic": "unidic_lite"
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},
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"name_or_path": "cl-tohoku/bert-base-japanese-v2",
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"never_split": null,
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"pad_token": "[PAD]",
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| 13 |
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"sep_token": "[SEP]",
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| 14 |
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"special_tokens_map_file": null,
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| 15 |
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"subword_tokenizer_type": "wordpiece",
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| 16 |
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"tokenizer_class": "BertJapaneseTokenizer",
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| 17 |
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"unk_token": "[UNK]",
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"word_tokenizer_type": "mecab"
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}
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train_results.json
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{
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"epoch": 3.0,
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"train_loss": 0.35883062176062114,
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| 4 |
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"train_runtime": 31863.0731,
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| 5 |
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"train_samples": 533005,
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| 6 |
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"train_samples_per_second": 50.184,
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| 7 |
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"train_steps_per_second": 1.568
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}
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trainer_state.json
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training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:fb9f2f86f316b0515d008676549c9f639b71284a1633505c7215f8732ef179ec
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| 3 |
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size 3311
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vocab.txt
ADDED
|
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|
|