Model Trained Using AutoTrain

Problem type: Multi-class Classification
Model ID: 2451975973
CO2 Emissions (in grams): 6.9906

Validation Metrics

Loss: 0.046
Accuracy: 0.989
Macro F1: 0.936
Micro F1: 0.989
Weighted F1: 0.989
Macro Precision: 0.929
Micro Precision: 0.989
Weighted Precision: 0.989
Macro Recall: 0.943
Micro Recall: 0.989
Weighted Recall: 0.989

Usage

This model has been trained to predict whether an article from a historic newspaper is a 'recipe' or 'not a recipe'. This model was trained on data generated by carrying out a keyword search of food terms and annotating examples results to indicate whether they were a recipe.

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/davanstrien/autotrain-recipes-2451975973

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("davanstrien/autotrain-recipes-2451975973", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-recipes-2451975973", use_auth_token=True)

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

outputs = model(**inputs)