
# 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)
```
{"language": "unk", "tags": "biobert", "datasets": ["Mim/autotrain-data-biobert-procell"], "widget": [{"text": "Cell lines expressing proteins \ud83e\udd17"}], "co2_eq_emissions": 0.5988414315305852}
transformers pytorch bert text-classification biobert unk dataset:Mim/autotrain-data-biobert-procell co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #biobert #unk #dataset-Mim/autotrain-data-biobert-procell #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# 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:



Or Python API:


# 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:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #biobert #unk #dataset-Mim/autotrain-data-biobert-procell #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # 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:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #biobert #unk #dataset-Mim/autotrain-data-biobert-procell #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# 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:



Or Python API: