README.md

---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: I love AutoTrain
datasets:
- NicholasSynovic/autotrain-data-luc-comp429-victorian-authorship-classification
co2_eq_emissions:
  emissions: 4.1359796275464005
license: agpl-3.0
metrics:
- accuracy
- f1
- recall
- bertscore
pipeline_tag: text-classification
---

# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 52472123757
- CO2 Emissions (in grams): 4.1360

This model reuses and extends a Bert model trained on [NicholasSynovic/Free-AutoTrain-VEAA](https://huggingface.co/datasets/NicholasSynovic/Free-AutoTrain-VEAA)

## Validation Metrics

- Loss: 1.425
- Accuracy: 0.636
- Macro F1: 0.504
- Micro F1: 0.636
- Weighted F1: 0.624
- Macro Precision: 0.523
- Micro Precision: 0.636
- Weighted Precision: 0.630
- Macro Recall: 0.508
- Micro Recall: 0.636
- Weighted Recall: 0.636


## 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/NicholasSynovic/autotrain-luc-comp429-victorian-authorship-classification-52472123757
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("NicholasSynovic/AutoTrain-LUC-COMP429-VEAA-Classification", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("NicholasSynovic/autotrain-luc-comp429-victorian-authorship-classification-52472123757", use_auth_token=True)

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

outputs = model(**inputs)
```