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--- |
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tags: autotrain |
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language: unk |
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widget: |
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- text: "I love AutoTrain 🤗" |
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datasets: |
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- Amalq/autotrain-data-smm4h_large_roberta_clean |
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co2_eq_emissions: 9.123490454955585 |
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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: 874027878 |
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- CO2 Emissions (in grams): 9.123490454955585 |
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## Validation Metrics |
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- Loss: 0.35724225640296936 |
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- Accuracy: 0.8571428571428571 |
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- Precision: 0.7637362637362637 |
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- Recall: 0.8910256410256411 |
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- AUC: 0.9267555361305361 |
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- F1: 0.8224852071005917 |
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## Usage |
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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/Amalq/autotrain-smm4h_large_roberta_clean-874027878 |
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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("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", 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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``` |
