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1000_respostas-MODELO_2
pedro-m4u/1000_respostas-MODELO_2
1000_respostas-MODELO_2 is a text classification model from pedro-m4u. 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: 3526695346 - CO2 Emissions (in grams): 1.7142
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Repo size
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Feb 16, 2023
- Updated
- Feb 16, 2023
- SHA
- 1e052303
Try a prompt
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3526695346
- CO2 Emissions (in grams): 1.7142
Validation Metrics
- Loss: 0.483
- Accuracy: 0.863
- Macro F1: 0.821
- Micro F1: 0.863
- Weighted F1: 0.858
- Macro Precision: 0.876
- Micro Precision: 0.863
- Weighted Precision: 0.866
- Macro Recall: 0.813
- Micro Recall: 0.863
- Weighted Recall: 0.863
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/pedro-m4u/autotrain-new_1000_respostas-3526695346
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("pedro-m4u/autotrain-new_1000_respostas-3526695346", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("pedro-m4u/autotrain-new_1000_respostas-3526695346", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- pt
- Created
- Feb 16, 2023
- Updated
- Feb 16, 2023