Model Card for Model ID

Model Details

elyzaタスク100のタスクのinputを入力してタスクを分類するためのタスクです。 タスクの分類は以下のものです。

• 知識説明型 Knowledge Explanation
• 創作型 Creative Generation
• 分析推論型 Analytical Reasoning
• 課題解決型 Task Solution
• 情報抽出型 Information Extraction
• 計算・手順型 Step-by-Step Calculation
• 意見・視点型 Opinion-Perspective
• ロールプレイ型 Role-Play Response

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]
• Shared by [optional]: [More Information Needed]
• Model type: [BERT]
• Language(s) (NLP): [Japanese]
• License: [More Information Needed]
• Finetuned from model [optional]: [cl-tohoku/bert-base-japanese-v3]

Direct Use

from transformers import pipeline

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,
 }
 )

Base model

tohoku-nlp/bert-base-japanese-v3

(48)