Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use javilonso/rtmex23-pol2-ptl_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use javilonso/rtmex23-pol2-ptl_v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="javilonso/rtmex23-pol2-ptl_v5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("javilonso/rtmex23-pol2-ptl_v5") model = AutoModelForSequenceClassification.from_pretrained("javilonso/rtmex23-pol2-ptl_v5") - Notebooks
- Google Colab
- Kaggle
rtmex23-pol2-ptl_v5
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-base-bne on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9794
- F1: 0.8604
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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.6419 | 1.0 | 17996 | 0.5762 | 0.7539 |
| 0.3916 | 2.0 | 35992 | 0.4623 | 0.8186 |
| 0.2182 | 3.0 | 53988 | 0.6224 | 0.8508 |
| 0.0796 | 4.0 | 71984 | 0.9794 | 0.8604 |
Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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