bert-base-uncased-finetuned-srl_arg

bert-base-uncased-finetuned-srl_arg

This model is a baseline 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: 0.1094
• Precision: 0.8207
• Recall: 0.8310
• F1: 0.8259
• Accuracy: 0.9722

Model description

The appraoch used for the baseline model is basically converting the sentence into the following form:

[CLS] This is the sentence content [SEP] is [SEP].

And this is realized by simply using the logic of the auto tokenizer: tokenizer(list1,list2) will return [CLS] list1 content [SEP] list2 content [SEP].

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