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ClaimBuster-DeBERTaV2
whispAI/ClaimBuster-DeBERTaV2
ClaimBuster-DeBERTaV2 is a text classification model from whispAI. 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: 3165789318 - CO2 Emissions (in grams): 23.1023
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552K
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Repo size
1.5 GB
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2
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Repo makeup
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.bin738 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Jan 30, 2023
- Updated
- Jan 30, 2023
- SHA
- c11ea472
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3165789318
- CO2 Emissions (in grams): 23.1023
Validation Metrics
- Loss: 0.405
- Accuracy: 0.842
- Macro F1: 0.753
- Micro F1: 0.842
- Weighted F1: 0.843
- Macro Precision: 0.750
- Micro Precision: 0.842
- Weighted Precision: 0.844
- Macro Recall: 0.756
- Micro Recall: 0.842
- Weighted Recall: 0.842
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/lucafrost/ClaimBuster-DeBERTaV2
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucafrost/ClaimBuster-DeBERTaV2", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucafrost/ClaimBuster-DeBERTaV2", 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 30, 2023
- Updated
- Jan 30, 2023