Instructions to use gastonstrizzolo/bert_adaptation_martin_fierro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gastonstrizzolo/bert_adaptation_martin_fierro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gastonstrizzolo/bert_adaptation_martin_fierro")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gastonstrizzolo/bert_adaptation_martin_fierro") model = AutoModelForMaskedLM.from_pretrained("gastonstrizzolo/bert_adaptation_martin_fierro") - Notebooks
- Google Colab
- Kaggle
bert_adaptation_martin_fierro
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.1933
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.3158 | 1.0 | 29 | 4.4719 |
| 4.1329 | 2.0 | 58 | 4.0855 |
| 3.7257 | 3.0 | 87 | 4.1261 |
| 3.3603 | 4.0 | 116 | 3.9543 |
| 3.1951 | 5.0 | 145 | 4.2631 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for gastonstrizzolo/bert_adaptation_martin_fierro
Base model
dccuchile/bert-base-spanish-wwm-uncased