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--- |
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tags: biobert |
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language: unk |
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widget: |
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- text: "Cell lines expressing proteins 🤗" |
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datasets: |
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- Mim/autotrain-data-biobert-procell |
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co2_eq_emissions: 0.5988414315305852 |
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--- |
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# Model Trained Using biobert |
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- Problem type: Binary Classification |
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- Model ID: 896229149 |
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- CO2 Emissions (in grams): 0.5988414315305852 |
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## Validation Metrics |
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- Loss: 0.4045306444168091 |
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- Accuracy: 0.8028169014084507 |
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- Precision: 0.8070175438596491 |
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- Recall: 0.9387755102040817 |
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- AUC: 0.8812615955473099 |
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- F1: 0.8679245283018868 |
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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": "Cell lines expressing proteins"}' https://api-inference.huggingface.co/models/Mim/autotrain-biobert-procell-896229149 |
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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("Mim/autotrain-biobert-procell-896229149", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-biobert-procell-896229149", use_auth_token=True) |
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inputs = tokenizer("Cell lines expressing proteins", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
