Instructions to use enriquesaou/roberta_en_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enriquesaou/roberta_en_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="enriquesaou/roberta_en_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("enriquesaou/roberta_en_v1") model = AutoModelForQuestionAnswering.from_pretrained("enriquesaou/roberta_en_v1") - Notebooks
- Google Colab
- Kaggle
roberta_en_v1
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: 1.5448
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|
| No log | 1.0 | 204 | 1.7931 |
| No log | 2.0 | 408 | 1.5597 |
| 2.0368 | 3.0 | 612 | 1.5448 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 4
Model tree for enriquesaou/roberta_en_v1
Base model
FacebookAI/roberta-base