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Datasets:
nuhuibrahim
/
recifinegold

Tasks:
Token Classification
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json
Languages:
English
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< 1K
Tags:
token-classification
named-entity-recognition
bert
roberta
recipes
knowledge-augmented
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pandas
Polars
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@@ -29,6 +29,27 @@ Each recipe in the dataset consists of:
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  ## Performance of Encoder Models Trained on The ReciFineGold Dataset
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  ## Entity Types
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  The dataset defines the following semantic entity types that were adopted from the [Recipe Case Study Paper](https://www.dl.soc.i.kyoto-u.ac.jp/~tajima/papers/hmdata18yamakatawww.pdf), each representing a different aspects of the cooking process:
 
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  ## Performance of Encoder Models Trained on The ReciFineGold Dataset
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+ <!-- |Model Type| Task Formulation | Knowledge Type | Precision (%) | Recall (%) | F1 (%) |
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+ |----------|---------------------|--------------------------|---------------|------------|--------|
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+ | | Traditional | --- | 89.51 | 90.26 | 89.88 |
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+ | |---------------------|--------------------------|---------------|------------|--------|
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+ | | | ETN | 95.41 | 94.37 | 94.88 |
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+ | BERT | Knowledge Augmented | QTN | 94.99 | 94.80 | 94.89 |
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+ | | | DTN | 94.65 | 93.88 | 94.26 |
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+ | | | EXT | 95.13 | 93.84 | 94.48 |
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+ | | | CBD | 94.62 | 94.44 | 94.53 |
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+ |----------|---------------------|--------------------------|---------------|------------|--------|
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+ | | Traditional | --- | 88.76 | 89.53 | 89.14 |
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+ | |---------------------|--------------------------|---------------|------------|--------|
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+ | | | ETN | 96.49 | 96.02 | 96.25 |
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+ | RoBERTa | Knowledge Augmented | QTN | 96.62 | 96.15 | 96.39 |
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+ | | | DTN | 96.27 | 95.92 | 96.09 |
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+ | | | EXT | 96.59 | 96.08 | 96.34 |
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+ | | | CBD | 90.54 | 89.33 | 89.93 |
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+ |----------|---------------------|--------------------------|---------------|------------|--------| -->
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+ ![Screenshot 2026-01-29 at 13.48.41](https://cdn-uploads.huggingface.co/production/uploads/6725049d93b72c9eaf7e075e/Pc4uuOpiuQEM7R387hjf5.png)
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  ## Entity Types
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  The dataset defines the following semantic entity types that were adopted from the [Recipe Case Study Paper](https://www.dl.soc.i.kyoto-u.ac.jp/~tajima/papers/hmdata18yamakatawww.pdf), each representing a different aspects of the cooking process: