Instructions to use BraveOni/2ch-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BraveOni/2ch-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BraveOni/2ch-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BraveOni/2ch-text-classification") model = AutoModelForSequenceClassification.from_pretrained("BraveOni/2ch-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from BraveOni/2ch-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
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https://huggingface.co/BraveOni/2ch-text-classification/resolve/main/README.md
- Command line
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hf download hf://BraveOni/2ch-text-classification/README.md
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curl -L -o README.md https://huggingface.co/BraveOni/2ch-text-classification/resolve/main/README.md
1.24 kB
metadata
tags: autotrain
language: en
widget:
- text: I love AutoTrain 🤗
datasets:
- BraveOni/autotrain-data-2ch-text-classification
co2_eq_emissions: 0.08564281067919652
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 955631800
- CO2 Emissions (in grams): 0.08564281067919652
Validation Metrics
- Loss: 0.34108611941337585
- Accuracy: 0.8671983356449375
- Precision: 0.7883283877349159
- Recall: 0.8250517598343685
- AUC: 0.9236450689447471
- F1: 0.8062721294891249
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/BraveOni/autotrain-2ch-text-classification-955631800
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("BraveOni/autotrain-2ch-text-classification-955631800", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("BraveOni/autotrain-2ch-text-classification-955631800", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)