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autotrain-ag-news-classification-2680881849
Hrishikesh332/autotrain-ag-news-classification-2680881849
autotrain-ag-news-classification-2680881849 is a text classification model from Hrishikesh332. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Multi-class Classification - Model ID: 2680881849 - CO2 Emissions (in grams): 12.8065
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Jan 7, 2023
- Updated
- Jan 7, 2023
- SHA
- 1284492e
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2680881849
- CO2 Emissions (in grams): 12.8065
Validation Metrics
- Loss: 0.390
- Accuracy: 0.882
- Macro F1: 0.765
- Micro F1: 0.882
- Weighted F1: 0.880
- Macro Precision: 0.927
- Micro Precision: 0.882
- Weighted Precision: 0.887
- Macro Recall: 0.705
- Micro Recall: 0.882
- Weighted Recall: 0.882
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/Hrishikesh332/autotrain-ag-news-classification-2680881849
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Hrishikesh332/autotrain-ag-news-classification-2680881849", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Hrishikesh332/autotrain-ag-news-classification-2680881849", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- Languages
- en
- Created
- Jan 7, 2023
- Updated
- Jan 7, 2023