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--- |
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tags: autotrain |
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language: en |
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widget: |
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- text: "Bill Gates wants to use mass Covid-19 vaccination campaign to implant microchips to track people" |
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datasets: |
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- Fake and real news datasets by CLÉMENT BISAILLON |
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co2_eq_emissions: 4.415122243239347 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem: Fake News Classification |
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- Problem type: Binary Classification |
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- Model ID: 785124234 |
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- CO2 Emissions (in grams): 4.415122243239347 |
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## Validation Metrics |
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- Loss: 0.00012586714001372457 |
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- Accuracy: 0.9998886538247411 |
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- Precision: 1.0 |
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- Recall: 0.9997665732959851 |
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- AUC: 0.9999999999999999 |
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- F1: 0.999883273024396 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("Nithiwat/autotrain-fake-news-classifier-785124234", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
