minilm-finetuned-movie

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minilm-finetuned-movie

This model is a fine-tuned version of 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

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1
• Datasets 2.12.0
• Tokenizers 0.13.2