library_name: transformers
tags:
- code
datasets:
- elyza/ELYZA-tasks-100
language:
- ja
metrics:
- accuracy
base_model:
- tohoku-nlp/bert-base-japanese-v3
pipeline_tag: text-classification
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Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [Hiroki Yanagisawa]
- Funded by [optional]: [More Information Needed]
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- Model type: [BERT]
- Language(s) (NLP): [Japanese]
- License: [More Information Needed]
- Finetuned from model [optional]: [cl-tohoku/bert-base-japanese-v3]
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Direct Use
from transformers import pipeline
このlabel2idで学習しました。label2idはこれを利用してください。 label2id = {'Task_Solution': 0, 'Creative_Generation': 1, 'Knowledge_Explanation': 2, 'Analytical_Reasoning': 3, 'Information_Extraction': 4, 'Step_by_Step_Calculation': 5, 'Role_Play_Response': 6, 'Opinion_Perspective': 7}
def preprocess_text_classification(examples: dict[str, list]) -> BatchEncoding: """バッチ処理用に修正""" encoded_examples = tokenizer( examples["questions"], # バッチ処理なのでリストで渡される max_length=512, padding=True, truncation=True, return_tensors=None # バッチ処理時はNoneを指定 )
# ラベルをバッチで数値に変換
encoded_examples["labels"] = [label2id[label] for label in examples["labels"]]
return encoded_examples
##使用するデータセット test_data = test_data.to_pandas() test_data["labels"] = test_data["labels"].apply(lambda x: label2id[x]) test_data
model_name = "hiroki-rad/bert-base-classification-ft" classify_pipe = pipeline(model=model_name, device="cuda:0")
class_label = dataset["labels"].unique() label2id = {label: id for id, label in enumerate(class_label)} id2label = {id: label for id, label in enumerate(class_label)}
results: list[dict[str, float | str]] = []
for i, example in tqdm(enumerate(test_data.itertuples())): # モデルの予測結果を取得 model_prediction = classify_pipe(example.questions)[0] # 正解のラベルIDをラベル名に変換 true_label = id2label[example.labels]
results.append(
{
"example_id": i,
"pred_prob": model_prediction["score"],
"pred_label": model_prediction["label"],
"true_label": true_label,
}
)
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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