File size: 3,650 Bytes
0fa60dd 4d3054d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/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](https://ibm.github.io/model-recycling/model_gain_chart?avg=1.66&mnli_lp=nan&20_newsgroup=-0.18&ag_news=0.31&amazon_reviews_multi=0.01&anli=1.65&boolq=4.52&cb=9.73&cola=0.80&copa=0.85&dbpedia=0.10&esnli=-0.15&financial_phrasebank=12.94&imdb=0.15&isear=1.24&mnli=-0.46&mrpc=2.61&multirc=0.49&poem_sentiment=0.58&qnli=0.59&qqp=0.20&rotten_tomatoes=-0.21&rte=2.35&sst2=0.60&sst_5bins=0.81&stsb=1.71&trec_coarse=0.17&trec_fine=7.02&tweet_ev_emoji=0.74&tweet_ev_emotion=0.19&tweet_ev_hate=2.17&tweet_ev_irony=1.77&tweet_ev_offensive=-0.41&tweet_ev_sentiment=0.88&wic=0.58&wnli=4.01&wsc=1.54&yahoo_answers=-0.16&model_name=momtaz%2Fbert-finetuned-squad&base_name=bert-base-cased) 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](https://ibm.github.io/model-recycling/)
|