Bert Base Uncased Finetuned Advanced Srl Arg

🚀 bert-base-uncased-finetuned-advanced-srl_arg

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/bert - base - uncased) for the Semantics Role Labeling (SRL) task on the English Universal Propbank dataset, achieving high - performance results.

🚀 Quick Start

This model is a fine - tuned version of [bert - base - uncased](https://huggingface.co/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

✨ Features

• Advanced SRL Approach: This more advanced SRL model uses a similar approach as the Augment method described in [NegBERT (Khandelwal, et al. 2020)](http://www.lrec - conf.org/proceedings/lrec2020/pdf/2020.lrec - 1.704.pdf). It adds a special token ([V]) immediately before the predicate.
• Whole Token Consideration: The special token and the predicate are considered a whole, which affects how the model processes sentences.

📚 Documentation

Model description

This more advanced SRL model uses a similar approach as the Augment method described in [NegBERT (Khandelwal, et al. 2020)](http://www.lrec - conf.org/proceedings/lrec2020/pdf/2020.lrec - 1.704.pdf). 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
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.37.0
• Pytorch 2.0.1+cu117
• Datasets 2.16.1
• Tokenizers 0.15.1

📄 License

This model is licensed under the Apache - 2.0 license.