|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
model-index: |
|
- name: minilm-finetuned-movie |
|
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. --> |
|
|
|
# minilm-finetuned-movie |
|
|
|
This model is a fine-tuned version of [microsoft/miniLM-L12-H384-uncased](https://huggingface.co/microsoft/miniLM-L12-H384-uncased) on sasingh192/movie-review dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0451 |
|
- F1: 0.9856 |
|
|
|
## Model description |
|
|
|
This model can be used to categorize a movie review into of the following categories: |
|
0 - negative |
|
1 - somewhat negative |
|
2 - neutral |
|
3 - somewhat positive |
|
4 - positive |
|
|
|
## Intended uses & limitations |
|
|
|
The fined model is based on the finetuning of the model devloped by Wang et al. |
|
|
|
@misc{wang2020minilm, |
|
title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers}, |
|
author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou}, |
|
year={2020}, |
|
eprint={2002.10957}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
|
|
|
|
## Training and evaluation data |
|
|
|
sasingh192/movie-review dataset contains a column 'TrainValTest'. The values provied in this columns are 'Train', 'Val', and 'Test'. |
|
The dataset can be filtered for the 'Train' values to train the model. Evaluation can be perfored on the data filtered by 'Val'. 'Test' is used as a blind test for kaggle. |
|
|
|
## Training procedure |
|
|
|
Training details are listed below. |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 0.9623 | 1.0 | 1946 | 0.7742 | 0.6985 | |
|
| 0.7969 | 2.0 | 3892 | 0.7289 | 0.7094 | |
|
| 0.74 | 3.0 | 5838 | 0.6479 | 0.7476 | |
|
| 0.7012 | 4.0 | 7784 | 0.6263 | 0.7550 | |
|
| 0.6689 | 5.0 | 9730 | 0.5823 | 0.7762 | |
|
| 0.6416 | 6.0 | 11676 | 0.5796 | 0.7673 | |
|
| 0.6149 | 7.0 | 13622 | 0.5324 | 0.7912 | |
|
| 0.5939 | 8.0 | 15568 | 0.5189 | 0.7986 | |
|
| 0.5714 | 9.0 | 17514 | 0.4793 | 0.8184 | |
|
| 0.5495 | 10.0 | 19460 | 0.4566 | 0.8249 | |
|
| 0.5297 | 11.0 | 21406 | 0.4155 | 0.8475 | |
|
| 0.5101 | 12.0 | 23352 | 0.4063 | 0.8494 | |
|
| 0.4924 | 13.0 | 25298 | 0.3829 | 0.8571 | |
|
| 0.4719 | 14.0 | 27244 | 0.4032 | 0.8449 | |
|
| 0.4552 | 15.0 | 29190 | 0.3447 | 0.8720 | |
|
| 0.4382 | 16.0 | 31136 | 0.3581 | 0.8610 | |
|
| 0.421 | 17.0 | 33082 | 0.3095 | 0.8835 | |
|
| 0.4038 | 18.0 | 35028 | 0.2764 | 0.9002 | |
|
| 0.3883 | 19.0 | 36974 | 0.2610 | 0.9051 | |
|
| 0.3745 | 20.0 | 38920 | 0.2533 | 0.9064 | |
|
| 0.3616 | 21.0 | 40866 | 0.2601 | 0.9005 | |
|
| 0.345 | 22.0 | 42812 | 0.2085 | 0.9267 | |
|
| 0.3314 | 23.0 | 44758 | 0.2421 | 0.9069 | |
|
| 0.3178 | 24.0 | 46704 | 0.2006 | 0.9268 | |
|
| 0.3085 | 25.0 | 48650 | 0.1846 | 0.9326 | |
|
| 0.2964 | 26.0 | 50596 | 0.1492 | 0.9490 | |
|
| 0.2855 | 27.0 | 52542 | 0.1664 | 0.9376 | |
|
| 0.2737 | 28.0 | 54488 | 0.1309 | 0.9560 | |
|
| 0.2641 | 29.0 | 56434 | 0.1318 | 0.9562 | |
|
| 0.2541 | 30.0 | 58380 | 0.1490 | 0.9440 | |
|
| 0.2462 | 31.0 | 60326 | 0.1195 | 0.9575 | |
|
| 0.234 | 32.0 | 62272 | 0.1054 | 0.9640 | |
|
| 0.2273 | 33.0 | 64218 | 0.1054 | 0.9631 | |
|
| 0.2184 | 34.0 | 66164 | 0.0971 | 0.9662 | |
|
| 0.214 | 35.0 | 68110 | 0.0902 | 0.9689 | |
|
| 0.2026 | 36.0 | 70056 | 0.0846 | 0.9699 | |
|
| 0.1973 | 37.0 | 72002 | 0.0819 | 0.9705 | |
|
| 0.1934 | 38.0 | 73948 | 0.0810 | 0.9716 | |
|
| 0.1884 | 39.0 | 75894 | 0.0724 | 0.9746 | |
|
| 0.1796 | 40.0 | 77840 | 0.0737 | 0.9743 | |
|
| 0.1779 | 41.0 | 79786 | 0.0665 | 0.9773 | |
|
| 0.1703 | 42.0 | 81732 | 0.0568 | 0.9811 | |
|
| 0.1638 | 43.0 | 83678 | 0.0513 | 0.9843 | |
|
| 0.1601 | 44.0 | 85624 | 0.0575 | 0.9802 | |
|
| 0.1593 | 45.0 | 87570 | 0.0513 | 0.9835 | |
|
| 0.1559 | 46.0 | 89516 | 0.0474 | 0.9851 | |
|
| 0.1514 | 47.0 | 91462 | 0.0477 | 0.9847 | |
|
| 0.1473 | 48.0 | 93408 | 0.0444 | 0.9858 | |
|
| 0.1462 | 49.0 | 95354 | 0.0449 | 0.9855 | |
|
| 0.1458 | 50.0 | 97300 | 0.0451 | 0.9856 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.2 |
|
- Pytorch 2.0.1 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.2 |
|
|
