Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("TamalDeFrijol/CompararTextos")
model = AutoModelForSequenceClassification.from_pretrained("TamalDeFrijol/CompararTextos")Quick Links
CompararTextos
This model is a fine-tuned version of distilroberta-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.8485
- Accuracy: 0.8137
- F1: 0.8746
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5557 | 1.09 | 500 | 0.6682 | 0.8211 | 0.8773 |
| 0.3543 | 2.18 | 1000 | 0.8485 | 0.8137 | 0.8746 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for TamalDeFrijol/CompararTextos
Base model
distilbert/distilroberta-baseDataset used to train TamalDeFrijol/CompararTextos
Evaluation results
- Accuracy on gluevalidation set self-reported0.814
- F1 on gluevalidation set self-reported0.875
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TamalDeFrijol/CompararTextos")