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)
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