Instructions to use arraypowerplay/mt5-small-amazon-reviews-es-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arraypowerplay/mt5-small-amazon-reviews-es-en with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="arraypowerplay/mt5-small-amazon-reviews-es-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("arraypowerplay/mt5-small-amazon-reviews-es-en") model = AutoModelForSeq2SeqLM.from_pretrained("arraypowerplay/mt5-small-amazon-reviews-es-en") - Notebooks
- Google Colab
- Kaggle
mt5-small-amazon-reviews-es-en
This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.9999
- Rouge1: 0.1983
- Rouge2: 0.0977
- Rougel: 0.1914
- Rougelsum: 0.1915
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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1050 | 3.1450 | 0.1738 | 0.0769 | 0.1669 | 0.1671 |
| 11.1901 | 2.0 | 2100 | 3.0413 | 0.1940 | 0.0942 | 0.1874 | 0.1874 |
| 11.1901 | 3.0 | 3150 | 2.9999 | 0.1983 | 0.0977 | 0.1914 | 0.1915 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for arraypowerplay/mt5-small-amazon-reviews-es-en
Base model
google/mt5-small