File size: 1,223 Bytes
73aad22 f2bab18 73aad22 f2bab18 73aad22 f2bab18 73aad22 f2bab18 73aad22 f2bab18 73aad22 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
tags: biobert
language: unk
widget:
- text: "Cell lines expressing proteins 🤗"
datasets:
- Mim/autotrain-data-biobert-procell
co2_eq_emissions: 0.5988414315305852
---
# 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)
``` |