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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-base-uncased-finetuned-recipe1m-ALL |
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results: [] |
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widget: |
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- text: "This is a great [MASK]." |
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--- |
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# RecipeBERT |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the food domain data [Recipe1M+ dataset](http://pic2recipe.csail.mit.edu/). |
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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](http://pic2recipe.csail.mit.edu/). |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6230 |
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## Usage |
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You can use this model to get embeddings/representations for your food-related dataset that you will use for your downstream tasks. |
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```python |
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from transformers import pipeline |
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# Your food-related data |
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food_data = "Hawaiian Pizza" |
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# Use pipeline for feature extraction |
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embedding = pipeline( |
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'feature-extraction', model='alexdseo/RecipeBERT', framework='pt' |
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) |
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# Mean pooling |
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food_rep = embedding(food_data, return_tensors='pt')[0].numpy().mean(axis=0) |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.7914 | 1.0 | 13286 | 0.7377 | |
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| 0.6945 | 2.0 | 26572 | 0.6569 | |
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| 0.6574 | 3.0 | 39858 | 0.6216 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.11.0 |
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- Tokenizers 0.14.1 |
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