# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Mim/biobert-procell-demo")
model = AutoModelForSequenceClassification.from_pretrained("Mim/biobert-procell-demo")Quick Links
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Model Trained Using biobert
- Problem type: Binary Classification
- Model ID: 896229149
- CO2 Emissions (in grams): 0.5988414315305852
Validation Metrics
- Loss: 0.4045306444168091
- Accuracy: 0.8028169014084507
- Precision: 0.8070175438596491
- Recall: 0.9387755102040817
- AUC: 0.8812615955473099
- F1: 0.8679245283018868
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": "Cell lines expressing proteins"}' https://api-inference.huggingface.co/models/Mim/autotrain-biobert-procell-896229149
Or Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-biobert-procell-896229149", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-biobert-procell-896229149", use_auth_token=True)
inputs = tokenizer("Cell lines expressing proteins", return_tensors="pt")
outputs = model(**inputs)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mim/biobert-procell-demo")