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taishi-i/awesome-japanese-nlp-classification-model

sourceHugging Faceotherupdated 3y agoView on Hugging Face

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Model overview

This model is the baseline model for awesome-japanese-nlp-classification-dataset. It was trained on this dataset, saved using the development data, and evaluated using the test data. The following table shows the evaluation results.

Label	Precision	Recall	F1-Score	Support
0	0.98	0.99	0.98	796
1	0.79	0.70	0.74	60
Accuracy			0.97	856
Macro Avg	0.89	0.84	0.86	856
Weighted Avg	0.96	0.97	0.97	856

Usage

Please install the following library.

bash

pip install transformers

You can easily use a classification model with the pipeline method.

python

from transformers import pipeline

pipe = pipeline(
    "text-classification",
    model="taishi-i/awesome-japanese-nlp-classification-model",
)

# Relevant sample
text = "ディープラーニングによる自然言語処理（共立出版）のサポートページです"
label = pipe(text)
print(label) # [{'label': '1', 'score': 0.9910495281219482}]

# Not Relevant sample
text = "AIイラストを管理するデスクトップアプリ"
label = pipe(text)
print(label) # [{'label': '0', 'score': 0.9986791014671326}]

Evaluation

Please install the following library.

bash

pip install evaluate scikit-learn datasets transformers torch

python

import evaluate
from datasets import load_dataset
from sklearn.metrics import classification_report
from transformers import pipeline

# Evaluation dataset
dataset = load_dataset("taishi-i/awesome-japanese-nlp-classification-dataset")

# Text classification model
pipe = pipeline(
    "text-classification",
    model="taishi-i/awesome-japanese-nlp-classification-model",
)

# Evaluation metric
f1 = evaluate.load("f1")

# Predict process
predicted_labels = []
for text in dataset["test"]["text"]:
    prediction = pipe(text)
    predicted_label = prediction[0]["label"]
    predicted_labels.append(int(predicted_label))

score = f1.compute(
    predictions=predicted_labels, references=dataset["test"]["label"]
)
print(score)

report = classification_report(
    y_true=dataset["test"]["label"], y_pred=predicted_labels
)
print(report)

License

This model was trained from a dataset collected from the GitHub API under GitHub Acceptable Use Policies - 7. Information Usage Restrictions and GitHub Terms of Service - H. API Terms. It should be used solely for research verification purposes. Adhering to GitHub's regulations is mandatory.