
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317

## Validation Metrics

- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259

## 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": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Mim/autotrain-procell-expert-800724769
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Mim/autotrain-procell-expert-800724769", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["Mim/autotrain-data-procell-expert"], "widget": [{"text": "ACE2 overexpression in AAV cell lines"}], "co2_eq_emissions": 0.004814823138367317}
transformers pytorch bert text-classification autotrain unk dataset:Mim/autotrain-data-procell-expert co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Mim/autotrain-data-procell-expert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317

## Validation Metrics

- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317 ## Validation Metrics

- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317 ## Validation Metrics

- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 800724769
- CO2 Emissions (in grams): 0.004814823138367317## Validation Metrics

- Loss: 0.4749071002006531
- Accuracy: 0.9
- Precision: 0.8928571428571429
- Recall: 0.9615384615384616
- AUC: 0.9065934065934066
- F1: 0.9259259259259259## Usage

You can use cURL to access this model:



Or Python API: