Instructions to use enriquesaou/roberta_es_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enriquesaou/roberta_es_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="enriquesaou/roberta_es_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("enriquesaou/roberta_es_v2") model = AutoModelForQuestionAnswering.from_pretrained("enriquesaou/roberta_es_v2") - Notebooks
- Google Colab
- Kaggle
roberta_es_v2
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5649
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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3046 | 1.0 | 724 | 1.6641 |
| 1.6565 | 2.0 | 1448 | 1.5655 |
| 1.3038 | 3.0 | 2172 | 1.5649 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for enriquesaou/roberta_es_v2
Base model
FacebookAI/roberta-base