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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: roberta-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: 0.9328 |
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verified: false |
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--- |
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# roberta-base-japanese-jsnli |
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This model is a fine-tuned version of [nlp-waseda/roberta-base-japanese](https://huggingface.co/nlp-waseda/roberta-base-japanese) 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: 0.2039 |
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- Accuracy: 0.9328 |
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### How to use the model |
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The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. |
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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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from pyknp import Juman |
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juman = Juman() |
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classifier = pipeline("zero-shot-classification", model="Formzu/roberta-base-japanese-jsnli") |
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sequence_to_classify = " ".join([tok.midasi for tok in juman.analysis("いつか世界を見る。").mrph_list()]) |
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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', '踊り', '料理'], |
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# 'scores': [0.8998081684112549, 0.06059670448303223, 0.03959512338042259]} |
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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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from pyknp import Juman |
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juman = Juman() |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model_name = "Formzu/roberta-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 = " ".join([tok.midasi for tok in juman.analysis("いつか世界を見る。").mrph_list()]) |
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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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#[0.82168734 0.1744363 0.00387629] [ 2.3362164 0.78641605 -3.0202653 ] |
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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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| 0.4067 | 1.0 | 16657 | 0.2224 | 0.9201 | |
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| 0.3397 | 2.0 | 33314 | 0.2152 | 0.9208 | |
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| 0.2775 | 3.0 | 49971 | 0.2039 | 0.9328 | |
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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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