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alpaca-bad-instruction-detector
dvilasuero/alpaca-bad-instruction-detector
alpaca-bad-instruction-detector is a text classification model from dvilasuero. 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: 46079114807 - CO2 Emissions (in grams): 0.4102
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Apr 3, 2023
- Updated
- Apr 3, 2023
- SHA
- 185679d2
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 46079114807
- CO2 Emissions (in grams): 0.4102
Validation Metrics
- Loss: 0.305
- Accuracy: 0.891
- Macro F1: 0.887
- Micro F1: 0.891
- Weighted F1: 0.891
- Macro Precision: 0.890
- Micro Precision: 0.891
- Weighted Precision: 0.891
- Macro Recall: 0.885
- Micro Recall: 0.891
- Weighted Recall: 0.891
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/dvilasuero/autotrain-alpaca-bs-detector-46079114807
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("dvilasuero/autotrain-alpaca-bs-detector-46079114807", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("dvilasuero/autotrain-alpaca-bs-detector-46079114807", 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
- Apr 3, 2023
- Updated
- Apr 3, 2023