metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
bert-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
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: 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: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
Model Recycling
Evaluation on 36 datasets using momtaz/bert-finetuned-squad as a base model yields average score of 74.08 in comparison to 72.43 by bert-base-cased.
The model is ranked 2nd among all tested models for the bert-base-cased architecture as of 22/01/2023 Results:
| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81.5587 | 89.3667 | 65.72 | 48.2188 | 72.7829 | 73.2143 | 82.6462 | 53 | 78.8667 | 89.4849 | 81.3 | 91.292 | 69.6219 | 82.9231 | 85.5392 | 60.953 | 68.2692 | 90.5913 | 90.1459 | 84.334 | 64.9819 | 92.0872 | 52.2172 | 86.2269 | 96.8 | 80 | 44.982 | 79.0289 | 54.9495 | 66.9643 | 83.8372 | 69.1062 | 65.3605 | 56.338 | 63.4615 | 70.8667 |
For more information, see: Model Recycling