bert base uncased finetuned advanced srl arg This model is a fine tuned version of bert base uncased on the English Universal Propbank dataset for the Semantics Role Labeling (SRL) task. It achieves the following results on the evaluation set: Loss: 0.0914 Precision: 0.8664 Recall: 0.8673 F1: 0.8669 Accuracy: 0.9812 Model description This more advanced SRL model uses similar apporach as the Augment method described in NegBERT (Khandelwal, et al. 2020). That is, adding a special token ([V]) immediately before the predicate: This [V] is a sentence. Note that the special token and the predicate is considered a whole . That is, the actual sentence is like 'This' '[V] is' 'a' 'sentence' '.' Usages The model labels semantics roles given input sentences. See usage examples at https://github.com/dannashao/bertsrl/blob/main/Evaluation.ipynb Training and evaluation data The English Universal Proposition Bank v1.0 data. See details at https://github.com/UniversalPropositions/UP 1.0 Training procedure See details at https://github.com/chuqiaog/Advanced NLP group 1/blob/main/A3/A3 main.ipynb Training hyperparameters The following hyperparameters were used during training: learning rate: 2e 05 t…
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