Instructions to use PanditaInfernal/punctuation_model_v3-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PanditaInfernal/punctuation_model_v3-long with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PanditaInfernal/punctuation_model_v3-long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
punctuation_model_v3-long
This model is a fine-tuned version of datificate/gpt2-small-spanish on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2654
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: 5e-05
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3789 | 1.0 | 750 | 0.2605 |
| 0.3067 | 2.0 | 1500 | 0.3096 |
| 0.1926 | 3.0 | 2250 | 0.2654 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
Inference Providers NEW
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Model tree for PanditaInfernal/punctuation_model_v3-long
Base model
datificate/gpt2-small-spanish