Instructions to use Sleoruiz/roberta-base-fine-tuned-text-classification-pesos-fixed-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sleoruiz/roberta-base-fine-tuned-text-classification-pesos-fixed-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sleoruiz/roberta-base-fine-tuned-text-classification-pesos-fixed-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sleoruiz/roberta-base-fine-tuned-text-classification-pesos-fixed-2") model = AutoModelForSequenceClassification.from_pretrained("Sleoruiz/roberta-base-fine-tuned-text-classification-pesos-fixed-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-base-fine-tuned-text-classification-pesos-fixed-2
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0640
- F1: 0.5201
- Accuracy: 0.3302
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.0626 | 1.0 | 6527 | 0.0628 | 0.3484 | 0.1556 |
| 0.0522 | 2.0 | 13054 | 0.0568 | 0.4758 | 0.2903 |
| 0.0389 | 3.0 | 19581 | 0.0581 | 0.5229 | 0.3294 |
| 0.0264 | 4.0 | 26108 | 0.0640 | 0.5201 | 0.3302 |
Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.3
- Downloads last month
- 6