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text_classification-roberta_base_sept2022
sabhashanki/text_classification-roberta_base_sept2022
text_classification-roberta_base_sept2022 is a text classification model from sabhashanki. 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: 2396974952 - CO2 Emissions (in grams): 2.0293
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At a glance
- Task
- Text Classification
- Library
- transformers
- Access
- Public
- Created
- Dec 9, 2022
- Updated
- Dec 14, 2022
- SHA
- 18937e15
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2396974952
- CO2 Emissions (in grams): 2.0293
Validation Metrics
- Loss: 0.656
- Accuracy: 0.846
- Macro F1: 0.826
- Micro F1: 0.846
- Weighted F1: 0.842
- Macro Precision: 0.867
- Micro Precision: 0.846
- Weighted Precision: 0.861
- Macro Recall: 0.829
- Micro Recall: 0.846
- Weighted Recall: 0.846
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/sabhashanki/autotrain-topic-prediction-latest-2396974952
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("sabhashanki/autotrain-topic-prediction-latest-2396974952", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("sabhashanki/autotrain-topic-prediction-latest-2396974952", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Languages
- unk
- Created
- Dec 9, 2022
- Updated
- Dec 14, 2022