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fake-news-debunker
Nithiwat/fake-news-debunker
fake-news-debunker is a text classification model from Nithiwat. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem: Fake News Classification - Problem type: Binary Classification - Model ID: 785124234 - CO2 Emissions (in grams): 4.415122243239347
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Repo size
657 MB
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2
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Apr 25, 2022
- Updated
- Apr 26, 2022
- SHA
- 8f22a53c
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem: Fake News Classification
- Problem type: Binary Classification
- Model ID: 785124234
- CO2 Emissions (in grams): 4.415122243239347
Validation Metrics
- Loss: 0.00012586714001372457
- Accuracy: 0.9998886538247411
- Precision: 1.0
- Recall: 0.9997665732959851
- AUC: 0.9999999999999999
- F1: 0.999883273024396
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/Nithiwat/autotrain-fake-news-classifier-785124234
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Languages
- en
- Created
- Apr 25, 2022
- Updated
- Apr 26, 2022