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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/mt5-small |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small |
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results: [] |
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datasets: |
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- srvmishra832/multilingual-amazon-reviews-6-languages |
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language: |
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- en |
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- de |
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--- |
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# Amazon_MultiLingual_Review_Summarization_with_google_mT5_small |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an Multi Lingual Amazon Reviews dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9368 |
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- Model Preparation Time: 0.0038 |
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- Rouge1: 16.1955 |
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- Rouge2: 8.1292 |
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- Rougel: 15.9218 |
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- Rougelsum: 15.9516 |
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## Model description |
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[google/mt5-small](https://huggingface.co/google/mt5-small) |
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## Intended uses & limitations |
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Multilingual Product Review Summarization. Supported Languages: English and German |
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## Training and evaluation data |
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The original multi-lingual Amazon product reviews dataset available on HuggingFace is defunct. |
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So, we use the version available at [Kaggle](https://www.kaggle.com/datasets/mexwell/amazon-reviews-multi). |
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The original dataset supports 6 languages: English, German, French, Spanish, Japanese, and Chamorro. |
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Each language has 20,000 training samples, 5,000 validation samples, and 5,000 testing samples. |
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We upload this dataset to HuggingFace hub at [srvmishra832/multilingual-amazon-reviews-6-languages](https://huggingface.co/datasets/srvmishra832/multilingual-amazon-reviews-6-languages) |
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Here, we only select the English and German language reviews for the `pc` and `electronics` product categories. |
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We use the review titles as summaries, and to prevent the model from generating very small summaries, we filter out those examples with extremely short review titles. |
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Finally, we downsample the resulting dataset so that training is feasible on the Google colab T4 GPU in a reasonable amount of time. |
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The final downsampled and concatenated dataset contains 8,000 training samples, 452 validation samples, and 422 test samples. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5.6e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:-------:|:------:|:-------:|:---------:| |
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| 9.0889 | 1.0 | 500 | 3.4117 | 0.0038 | 12.541 | 5.1023 | 11.9039 | 11.8749 | |
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| 4.3977 | 2.0 | 1000 | 3.1900 | 0.0038 | 15.342 | 6.747 | 14.9223 | 14.8598 | |
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| 3.9595 | 3.0 | 1500 | 3.0817 | 0.0038 | 15.3976 | 6.2063 | 15.0635 | 15.069 | |
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| 3.7525 | 4.0 | 2000 | 3.0560 | 0.0038 | 15.7991 | 6.8536 | 15.4657 | 15.5263 | |
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| 3.6191 | 5.0 | 2500 | 3.0048 | 0.0038 | 16.3791 | 7.3671 | 16.0817 | 16.059 | |
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| 3.5155 | 6.0 | 3000 | 2.9779 | 0.0038 | 16.2311 | 7.5629 | 15.7492 | 15.758 | |
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| 3.4497 | 7.0 | 3500 | 2.9663 | 0.0038 | 16.2554 | 8.1464 | 15.9499 | 15.9152 | |
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| 3.3889 | 8.0 | 4000 | 2.9438 | 0.0038 | 16.5764 | 8.3698 | 16.3225 | 16.2848 | |
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| 3.3656 | 9.0 | 4500 | 2.9365 | 0.0038 | 16.1416 | 8.0266 | 15.8921 | 15.8913 | |
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| 3.3562 | 10.0 | 5000 | 2.9368 | 0.0038 | 16.1955 | 8.1292 | 15.9218 | 15.9516 | |
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### Framework versions |
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- Transformers 4.50.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
