Instructions to use Sergio10IA/roberta-base-bne-Modelo_CF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sergio10IA/roberta-base-bne-Modelo_CF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Sergio10IA/roberta-base-bne-Modelo_CF")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Sergio10IA/roberta-base-bne-Modelo_CF") model = AutoModelForQuestionAnswering.from_pretrained("Sergio10IA/roberta-base-bne-Modelo_CF") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - sqac | |
| model-index: | |
| - name: roberta-base-bne-Modelo_CF | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # roberta-base-bne-Modelo_CF | |
| This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the sqac dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8967 | |
| ## 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: 2e-05 | |
| - train_batch_size: 12 | |
| - eval_batch_size: 12 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.93 | 1.0 | 1594 | 0.8531 | | |
| | 0.4279 | 2.0 | 3188 | 0.8967 | | |
| ### Framework versions | |
| - Transformers 4.29.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |