license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-condaqa-neg-tag-token-classifier
results: []
roberta-large-condaqa-neg-tag-token-classifier
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0268
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9899
Model description
Negation detector. A roberta used for detecting negation words in sentences. A negation word will get label "Y".
Intended uses & limitations
Because the training dataset is small and one sentence is long, maybe some short sentence detection is not that satisfing.
Training and evaluation data
Using negation annotation and sentence from CondaQA. You can get the dataset through both github and huggingface. github: https://github.com/AbhilashaRavichander/CondaQA Common negation cues in CondaQA: ['halt', 'inhospitable', 'unhappy', 'unserviceable', 'dislike', 'unaware', 'unfavorable', 'barely', 'unseen', 'unoccupied', 'unreliability', 'insulator', 'stop', 'indistinguishable', 'unrestricted', 'unfairly', 'unsupervised', 'unicameral', 'forbid', 'unforgettable', 'reject', 'uneducated', 'unlimited', 'illegal', 'uncertainty', 'nonhuman', 'unborn', 'unshaven', 'uncanny', 'incomplete', 'unsure', 'unconscious', 'atypical', 'indirectly', 'unloaded', 'disadvantage', 'contrary', 'infrequent', 'unofficial', 'few', 'untouched', 'refuse', 'inequitable', 'disproportionate', 'unexpected', 'displeased', 'unpaved', 'unwieldy', 'not at all', 'absent', 'unnoticed', 'unpleasant', 'unsafe', 'unsigned', 'not', 'inaccurate', 'cannot', 'involuntary', 'unequipped', 'illiterate', 'cease', 'disagreeable', 'prohibit', 'unable', 'unstable', 'uninhabited', 'unclean', 'useless', 'disapprove', 'insensitive', 'in the absence of', 'impractical', 'unorthodox', 'untreated', 'unsuccessful', 'unwitting', 'unfashionable', 'disagreement', 'unmyelinated', 'unfortunate', 'unknown', 'ineffective', 'a lack of', 'instead of', 'refused', 'illegitimate', 'little', 'unpaid', 'fail', 'unintentionally', 'unglazed', "didn't", 'unprocessed', 'inability', 'undeveloped', 'exclude', 'neither', 'except', 'unequivocal', 'unconventional', 'incorrectly', 'unconditional', 'prevent', 'dissimilar', 'uncommon', 'inorganic', 'unquestionable', 'uncoated', 'unassisted', 'unprecedented', 'nonviolent', 'unarmed', 'unpopular', 'inadequate', 'uncomfortable', 'unwilling', 'unaffected', 'unfaithful', 'nobody', 'loss', 'without', 'undamaged', 'nothing', 'could not', 'impossible to', 'unaccompanied', 'unlike', 'oppose', 'compromising', 'unmarried', 'rarely', 'unlighted', 'inexperienced', 'rather than', 'unrelated', 'untied', 'dishonest', 'insecure', 'uneven', 'harmless', 'avoid', 'with the exception of', 'no', 'undefeated', 'no longer', 'inadvertently', 'absence', 'lack', 'unconnected', 'unfinished', 'invalid', 'unnecessary', 'invisibility', 'unusual', 'none', 'incredulous', 'impossible', 'never', 'untrained', 'incorrect', 'immobility', 'unclear', 'impartial', 'unlucky', 'deny', 'uncertain', 'hardly', 'unsaturated', 'informal', 'irregular', 'dissatisfaction']
Training procedure
Use code from huggingface source
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 4 | 0.1526 | 0.0 | 0.0 | 0.0 | 0.9588 |
| No log | 2.0 | 8 | 0.0875 | 0.0 | 0.0 | 0.0 | 0.9588 |
| No log | 3.0 | 12 | 0.0396 | 0.0 | 0.0 | 0.0 | 0.9877 |
| No log | 4.0 | 16 | 0.0322 | 0.0 | 0.0 | 0.0 | 0.9899 |
| No log | 5.0 | 20 | 0.0270 | 0.0 | 0.0 | 0.0 | 0.9906 |
| No log | 6.0 | 24 | 0.0268 | 0.0 | 0.0 | 0.0 | 0.9899 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.10.1
- Datasets 2.6.1
- Tokenizers 0.13.1