alex2awesome / quote-type-sentence-model

quote-type-sentence-model

This model is a fine-tuned version of roberta-base to perform sentence level classification on a news dataset. The following sentence level tags are identified:

  'No Quote'
  'Direct Quote'
  'Published Work/Press Report'
  'Indirect Quote'
  'Statement/Public Speech'
  'Background/Narrative'
  'Other'
  'Proposal/Order/Law'
  'Email/Social Media Post'
  'Court Proceeding'
  'Direct Observation'

It achieves the following results on the evaluation set:

• Loss: 0.8795
• F1: 0.5407

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3