RecipeBERT
This model is a fine-tuned version of bert-base-uncased on the food domain data Recipe1M+ dataset. Recipe1M+ contains over 1M records of distinct food names with their ingredients and recipes, more details about the dataset can be found on their project website. We used the whole Recipe1M+ dataset with a total of 1,029,720 records, with using 10% of the dataset as an evaluation dataset. Each of the records contains the food name, followed by its ingredients and recipes.
It achieves the following results on the evaluation set:
- Loss: 0.6230
Usage
You can use this model to get embeddings/representations for your food-related dataset that you will use for your downstream tasks.
from transformers import pipeline
# Your food-related data
food_data = "Hawaiian Pizza"
# Use pipeline for feature extraction
embedding = pipeline(
'feature-extraction', model='alexdseo/RecipeBERT', framework='pt'
)
# Mean pooling
food_rep = embedding(food_data, return_tensors='pt')[0].numpy().mean(axis=0)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.7914 | 1.0 | 13286 | 0.7377 |
0.6945 | 2.0 | 26572 | 0.6569 |
0.6574 | 3.0 | 39858 | 0.6216 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.11.0
- Tokenizers 0.14.1
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Model tree for alexdseo/RecipeBERT
Base model
google-bert/bert-base-uncased