metadata

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

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)