bert-finetuned-AAVE-PoS
This model is a version of bert-base-cased fine-tuned on a dataset of African American Vernacular English (AAVE) which was published alongside Jørgensen et al. 2016. It achieves the following results on the evaluation set:
- Loss: 0.2582
- Precision: 0.8632
- Recall: 0.8730
- F1: 0.8681
- Accuracy: 0.9356
Model description
More information needed
Intended uses & limitations
This model is intended to help close the gap in part-of-speech tagging performance between Standard American English (SAE) and African American English (AAVE) which differ liguistically in many well-documented ways. It was fine-tuned on data gathered from Twitter, and is thus ingrained with what linguists call 'register bias'.
Training and evaluation data
Code hosted at GitHub.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 (this amount of data overfits on 3+)
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 223 | 0.2982 | 0.8196 | 0.8350 | 0.8272 | 0.9216 |
| No log | 2.0 | 446 | 0.2625 | 0.8599 | 0.8680 | 0.8640 | 0.9326 |
| 0.4647 | 3.0 | 669 | 0.2582 | 0.8632 | 0.8730 | 0.8681 | 0.9356 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
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