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--- |
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language: en |
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widget: |
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- text: "I love AutoTrain 🤗" |
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datasets: |
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- hsaglamlar/autotrain-data-stress_v2 |
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co2_eq_emissions: 2.7282806494855265 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Binary Classification |
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- Model ID: 1178743973 |
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- CO2 Emissions (in grams): 2.7282806494855265 |
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## Validation Metrics |
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- Loss: 0.431733638048172 |
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- Accuracy: 0.7976190476190477 |
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- Precision: 0.6918918918918919 |
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- Recall: 0.8205128205128205 |
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- AUC: 0.8952141608391608 |
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- F1: 0.7507331378299119 |
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## Usage |
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This model finds self-reported stress from txt. |
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You can use cURL to access this model: |
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``` |
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$ 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/hsaglamlar/autotrain-stress_v2-1178743973 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("hsaglamlar/autotrain-stress_v2-1178743973", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("hsaglamlar/autotrain-stress_v2-1178743973", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
