---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "a favourite dutch salad in the way some prime kippered herrings a dn d haddock or some fine yarmouth bloaters then when cold remove all the bones and skin aryl tear the flesh into shreds with two forks sea en these well with pepper salad oil and tarragea vinegar and set aside in a cool place until required cut up into small dice myrtle boil beetroot and potatoes raw cucumber and onions and mix well together with the fish and sonto wellmade tartar sauce then pile up the whols on a flat dish sprinkle well with a mixture of finelychopped parsley and sifted egg yolk garnish round the base with anchovy or saniino crodtons tastefully ornamented with tiny patches or chopped parsley and strips of hardboiled white of egg and servo"
- text: "collieries the men at one of the collieries have in times of scarcity been in the habit houseo f this getting from e v i t a a t t el s eve r a wellnt wishing g h e t r h e a r v e als water disturbed so frequently locked up the well one of the men a blacksmith removed fhe lock and subse quently received notice to leave the colliery the other mechanics decided that unless the the masters at once withdrew the blacksmiths notice they themselves would resign the masters however refused and a fortni"
datasets:
- davanstrien/autotrain-data-recipes
co2_eq_emissions:
  emissions: 6.990639915807625
---

# 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)
```