pytorch bert text-classification unk dataset:priyamm/autotrain-data-KeywordExtraction transformers autotrain co2_eq_emissions
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- priyamm/autotrain-data-KeywordExtraction
co2_eq_emissions: 0.21373468108000182
---

# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 882328335
- CO2 Emissions (in grams): 0.21373468108000182

## Validation Metrics

- Loss: 0.2641160488128662
- Accuracy: 0.9128
- Precision: 0.9444444444444444
- Recall: 0.8772
- AUC: 0.9709556000000001
- F1: 0.9095810866860223

## 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/priyamm/autotrain-KeywordExtraction-882328335
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("priyamm/autotrain-KeywordExtraction-882328335", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("priyamm/autotrain-KeywordExtraction-882328335", use_auth_token=True)

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

outputs = model(**inputs)
```