pytorch bert 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: 5.080390550458655
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 717221775
- CO2 Emissions (in grams): 5.080390550458655

## Validation Metrics

- Loss: 0.35279911756515503
- Accuracy: 0.9269102990033222
- Macro F1: 0.9261839948926327
- Micro F1: 0.9269102990033222
- Weighted F1: 0.9263981751760975
- Macro Precision: 0.9273912049203341
- Micro Precision: 0.9269102990033222
- Weighted Precision: 0.9280084437800646
- Macro Recall: 0.927250645380574
- Micro Recall: 0.9269102990033222
- Weighted Recall: 0.9269102990033222


## 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-717221775
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("palakagl/autotrain-PersonalAssitant-717221775", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("palakagl/autotrain-PersonalAssitant-717221775", use_auth_token=True)

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

outputs = model(**inputs)
```