persuasive_essays_distilbert_uncased
Model description
This model is a fine-tuned version of distilbert-base-uncased on the emnlp2017-claim-identification/persuasive_essays dataset. It achieves the following results on the evaluation set:
- Loss: 0.4314
- Accuracy: 0.8001
- Macro F1: 0.7510
- Claim F1: 0.6403
Intended uses & limitations
Text classification for claims on full sentences. The model perfoms better at in-domain classification. Cross-domain classification is severely limited.
Training and evaluation data
Based on Stab and Gurevych (2017) persuasive essays corpus, preprocessed by Daxenberger et al. (2017).
Original dataset
- docs: 402
- tokens: 147,271
- total instances: 7,116 (65 duplicates)
- #claims: 2,108 (29.62%)
Trimmed datast used for training
- total instances: 7051 (65 duplicates removed)
- #claims: 2093 (29.68%)
- train/test split: 80/20, stratified
Training procedure
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: 2
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Claim F1 |
---|---|---|---|---|---|---|
No log | 1.0 | 353 | 0.4486 | 0.7810 | 0.7447 | 0.6485 |
0.4521 | 2.0 | 706 | 0.4314 | 0.8001 | 0.7510 | 0.6403 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
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Base model
distilbert/distilbert-base-uncased