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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
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 0.19 | 100 | 1.1174 | 0.2831 |
| No log | 0.38 | 200 | 1.1066 | 0.3356 |
| No log | 0.57 | 300 | 1.0490 | 0.4126 |
| No log | 0.76 | 400 | 1.0280 | 0.3778 |
| 1.0973 | 0.95 | 500 | 0.9378 | 0.4492 |
| 1.0973 | 1.14 | 600 | 1.0546 | 0.4650 |
| 1.0973 | 1.33 | 700 | 0.9806 | 0.4619 |
| 1.0973 | 1.52 | 800 | 0.8989 | 0.5176 |
| 1.0973 | 1.7 | 900 | 0.9531 | 0.5078 |
| 0.8155 | 1.89 | 1000 | 0.9482 | 0.4781 |
| 0.8155 | 2.08 | 1100 | 0.8935 | 0.5084 |
| 0.8155 | 2.27 | 1200 | 0.9059 | 0.5236 |
| 0.8155 | 2.46 | 1300 | 0.9483 | 0.5127 |
| 0.8155 | 2.65 | 1400 | 0.8961 | 0.5355 |
| 0.6225 | 2.84 | 1500 | 0.8795 | 0.5407 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
