transformers pytorch safetensors bert text-classification autotrain unk dataset:GRPUI/autotrain-data-sgugit-model-v4 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- GRPUI/autotrain-data-sgugit-model-v4
co2_eq_emissions:
  emissions: 1.0134962279728574
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 92034144745
- CO2 Emissions (in grams): 1.0135

## Validation Metrics

- Loss: 0.027
- Accuracy: 0.997
- Macro F1: 0.989
- Micro F1: 0.997
- Weighted F1: 0.997
- Macro Precision: 0.991
- Micro Precision: 0.997
- Weighted Precision: 0.997
- Macro Recall: 0.989
- Micro Recall: 0.997
- Weighted Recall: 0.997


## 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/GRPUI/autotrain-sgugit-model-v4-92034144745
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("GRPUI/autotrain-sgugit-model-v4-92034144745", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("GRPUI/autotrain-sgugit-model-v4-92034144745", use_auth_token=True)

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

outputs = model(**inputs)
```