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autotrain-hvsm_textclassification-51689122365
TaScheibe/autotrain-hvsm_textclassification-51689122365
autotrain-hvsm_textclassification-51689122365 is a text classification model from TaScheibe. Use it when you need a label for a piece of text. It is set up for transformers.
- Problem type: Binary Classification - Model ID: 51689122365 - CO2 Emissions (in grams): 0.0093
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Repo size
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.bin738 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Apr 22, 2023
- Updated
- Apr 22, 2023
- SHA
- 2a43c8d4
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 51689122365
- CO2 Emissions (in grams): 0.0093
Validation Metrics
- Loss: 0.178
- Accuracy: 0.936
- Precision: 0.922
- Recall: 0.952
- AUC: 0.982
- F1: 0.937
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/TaScheibe/autotrain-hvsm_textclassification-51689122365
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("TaScheibe/autotrain-hvsm_textclassification-51689122365", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("TaScheibe/autotrain-hvsm_textclassification-51689122365", 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 22, 2023
- Updated
- Apr 22, 2023