Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/roberta_Pfinal_4CLASES_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/roberta_Pfinal_4CLASES_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/roberta_Pfinal_4CLASES_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/roberta_Pfinal_4CLASES_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/roberta_Pfinal_4CLASES_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: roberta_Pfinal_4CLASES_2e-5_16_2 | |
| 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_Pfinal_4CLASES_2e-5_16_2 | |
| This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3321 | |
| - Accuracy: 0.9031 | |
| ## 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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.4479 | 1.0 | 669 | 0.3181 | 0.8927 | | |
| | 0.2679 | 2.0 | 1338 | 0.3321 | 0.9031 | | |
| ### Framework versions | |
| - Transformers 4.28.0 | |
| - Pytorch 1.13.1+cu117 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |