transformers pytorch safetensors deberta text-classification autotrain en dataset:kafikani/autotrain-data-dynex-bert-10.2023 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- kafikani/autotrain-data-dynex-bert-10.2023
co2_eq_emissions:
  emissions: 2.484897786762931
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 98307147045
- CO2 Emissions (in grams): 2.4849

## Validation Metrics

- Loss: 0.413
- Accuracy: 0.846
- Macro F1: 0.719
- Micro F1: 0.846
- Weighted F1: 0.839
- Macro Precision: 0.752
- Micro Precision: 0.846
- Weighted Precision: 0.838
- Macro Recall: 0.704
- Micro Recall: 0.846
- Weighted Recall: 0.846


## 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/kafikani/autotrain-dynex-bert-10.2023-98307147045
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("kafikani/autotrain-dynex-bert-10.2023-98307147045", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("kafikani/autotrain-dynex-bert-10.2023-98307147045", use_auth_token=True)

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

outputs = model(**inputs)
```