Instructions to use ignaciovillanueva/umt5-base-finetuned-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ignaciovillanueva/umt5-base-finetuned-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ignaciovillanueva/umt5-base-finetuned-model") model = AutoModelForSeq2SeqLM.from_pretrained("ignaciovillanueva/umt5-base-finetuned-model") - Notebooks
- Google Colab
- Kaggle
umt5-base-finetuned-model
This model is a fine-tuned version of google/umt5-base on the None 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: 0.0002
- train_batch_size: 12
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 100
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 3
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Model tree for ignaciovillanueva/umt5-base-finetuned-model
Base model
google/umt5-base