YAML Metadata
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Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182
Validation Metrics
- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214
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/lewtun/autotrain-my-eval-project-615-5694363
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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Evaluation results
- Loss on Emotiontest set self-reported0.174
- Accuracy on Emotiontest set self-reported0.927
- Macro F1 on Emotiontest set self-reported0.883
- Recall on Emotiontest set self-reported0.927
- Weighted F1 on Emotiontest set self-reported0.927
- Macro Precision on Emotiontest set self-reported0.888
- Micro Precision on Emotiontest set self-reported0.927
- Weighted Precision on Emotiontest set self-reported0.927
- Macro Recall on Emotiontest set self-reported0.879
- Micro Recall on Emotiontest set self-reported0.927