README.md

---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap
co2_eq_emissions: 16.816741650923202
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1022634735
- CO2 Emissions (in grams): 16.816741650923202

## Validation Metrics

- Loss: 0.4373569190502167
- Accuracy: 0.9027552674230146
- Macro F1: 0.8938134766263609
- Micro F1: 0.9027552674230146
- Weighted F1: 0.9023653852553881
- Macro Precision: 0.8970541297231431
- Micro Precision: 0.9027552674230146
- Weighted Precision: 0.903514305510645
- Macro Recall: 0.892665778987219
- Micro Recall: 0.9027552674230146
- Weighted Recall: 0.9027552674230146


## 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/justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634735", use_auth_token=True)

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

outputs = model(**inputs)
```