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Feature: Update model after changing the sentence_length feature to be a string in a13b3b0
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README.md

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@@ -48,13 +48,13 @@ The model has the following accuracy on a test data set of 20% of the total data
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```
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Sentence-level results:
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Accuracy: 94.66%
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Accuracy: 94.65%
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Word-level results:
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Accuracy 97.82%
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Precision (micro) 97.81%
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Precision (micro) 97.80%
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Recall (micro) 97.82%
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F1 score (micro) 97.81%
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F1 score (micro) 97.80%
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```
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## Development

ingredient_parser/en/ModelCard.en.md

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### Model Date and Version
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Date: April 2025
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Date: May 2025
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Version: The model version is the same has the `ingredient_parser_nlp` package version.
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| Word level accuracy | Sentence level accuracy |
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| ------------------- | ----------------------- |
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| 97.82 ± 0.18% | 94.62 ± 0.44% |
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| 97.82 ± 0.18% | 94.65 ± 0.44% |
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These metrics were determined by executing 20 training/evaluation cycles and calculating the mean and standard deviation for the two metrics across all cycles. The uncertainty values provided represent the 99.7% confidence bounds (i.e. 3x standard deviation). The uncertainty is due to the randomisation of the selection of training and evaluation data whenever the model is trained.
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tests/preprocess/test_preprocess.py

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@@ -107,7 +107,7 @@ def test(self):
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expected = [
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{
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"bias": "",
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"sentence_length": 4,
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"sentence_length": "4",
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"pos": "CD",
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"stem": "!num",
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"is_capitalised": False,
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},
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{
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"bias": "",
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"sentence_length": 4,
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"sentence_length": "4",
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"pos": "NN",
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"stem": "cup",
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"is_capitalised": False,
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},
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{
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"bias": "",
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"sentence_length": 4,
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"sentence_length": "4",
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"pos": "NN",
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"stem": "chicken",
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"is_capitalised": False,
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},
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{
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"bias": "",
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"sentence_length": 4,
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"sentence_length": "4",
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"pos": "NN",
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"stem": "broth",
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"is_capitalised": False,

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