pytorch distilbert text-classification en dataset:palakagl/autotrain-data-PersonalAssitant transformers autotrain co2_eq_emissions
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- palakagl/autotrain-data-PersonalAssitant
co2_eq_emissions: 2.258363491829382
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221781
- CO2 Emissions (in grams): 2.258363491829382

## Validation Metrics

- Loss: 0.38660314679145813
- Accuracy: 0.9042081949058693
- Macro F1: 0.9079200295131094
- Micro F1: 0.9042081949058692
- Weighted F1: 0.9052766730963512
- Macro Precision: 0.9116101664087508
- Micro Precision: 0.9042081949058693
- Weighted Precision: 0.9097680514456175
- Macro Recall: 0.9080246002936301
- Micro Recall: 0.9042081949058693
- Weighted Recall: 0.9042081949058693


## 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/palakagl/autotrain-PersonalAssitant-717221781
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221781", use_auth_token=True)

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

outputs = model(**inputs)
```