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  # req_mod_ner_modelv2
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- This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on a private dataset with 300 sentences/phrases with 1,954 token labels (IOB2 format) aimed at extracting software requirements related named entities. The following labels are used:
 
 
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  - Actor (used for all types of software users and groups of users)
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  - COTS (abbreviation for Commercial Off-The-Shelf Software)
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  - Function (used for functions, functionality, features)
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  ## Training and evaluation data
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- The model was trained on the req_mod_ner dataset. This dataset is private and contains 300 sentences/phrases and 1,954 IOB2 labels. The dataset is split 240/30/30 into train, validation and test. The reported metrics are from the evaluation on the test set. The validation set was used for cross-validation during training.
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- ## Training procedure
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  ### Training hyperparameters
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  # req_mod_ner_modelv2
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+ This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on a
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+ private dataset with 300 sentences/phrases with 1,954 token labels (IOB2 format) aimed at extracting software requirements
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+ related named entities. The following labels are used:
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  - Actor (used for all types of software users and groups of users)
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  - COTS (abbreviation for Commercial Off-The-Shelf Software)
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  - Function (used for functions, functionality, features)
 
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  ## Training and evaluation data
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+ The model was trained on the ReqModNer dataset. This dataset is private and contains 300 sentences/phrases and 1,954 IOB2 labels. The dataset is split 240/30/30 into train, validation and test. The reported metrics are from the evaluation on the test set. The validation set was used for cross-validation during training.
 
 
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  ### Training hyperparameters
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