---
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
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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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,
        }
    )

### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!https://huggingface.co/datasets/elyza/ELYZA-tasks-100>

[More Information Needed]

### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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