Instructions to use nicocaine/my_legal_mt5sum_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicocaine/my_legal_mt5sum_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nicocaine/my_legal_mt5sum_model") model = AutoModelForSeq2SeqLM.from_pretrained("nicocaine/my_legal_mt5sum_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_legal_mt5sum_model
This model is a fine-tuned version of google/mt5-large on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
Framework versions
- Transformers 4.48.2
- TensorFlow 2.18.0
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for nicocaine/my_legal_mt5sum_model
Base model
google/mt5-large