Instructions to use Clau31/practica8-neutralizacion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Clau31/practica8-neutralizacion with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Clau31/practica8-neutralizacion") model = AutoModelForSeq2SeqLM.from_pretrained("Clau31/practica8-neutralizacion") - Notebooks
- Google Colab
- Kaggle
practica8-neutralizacion
This model is a fine-tuned version of google/flan-t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.3360
- Bleu: 4.1523
- Gen Len: 19.1042
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 7.2323 | 1.0 | 440 | 6.6949 | 4.0862 | 19.1354 |
| 6.7399 | 2.0 | 880 | 6.3360 | 4.1523 | 19.1042 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
- 4
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Clau31/practica8-neutralizacion
Base model
google/flan-t5-small