Instructions to use Andreabp/mt5-small-finetuned-amazon-en-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andreabp/mt5-small-finetuned-amazon-en-es 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="Andreabp/mt5-small-finetuned-amazon-en-es")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Andreabp/mt5-small-finetuned-amazon-en-es") model = AutoModelForSeq2SeqLM.from_pretrained("Andreabp/mt5-small-finetuned-amazon-en-es") - Notebooks
- Google Colab
- Kaggle
mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of google/mt5-small on an unknown 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: 5.6e-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: 8
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Andreabp/mt5-small-finetuned-amazon-en-es
Base model
google/mt5-small