Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Dulfary/platzi-distilroberta-base-mrpc-glue-prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dulfary/platzi-distilroberta-base-mrpc-glue-prueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dulfary/platzi-distilroberta-base-mrpc-glue-prueba")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dulfary/platzi-distilroberta-base-mrpc-glue-prueba") model = AutoModelForSequenceClassification.from_pretrained("Dulfary/platzi-distilroberta-base-mrpc-glue-prueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
platzi-distilroberta-base-mrpc-glue-prueba
This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets. It achieves the following results on the evaluation set:
- Loss: 0.7472
- Accuracy: 0.8235
- F1: 0.8784
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 |
|---|---|---|---|---|---|
| 0.336 | 1.09 | 500 | 0.7472 | 0.8235 | 0.8784 |
| 0.1824 | 2.18 | 1000 | 0.9205 | 0.8235 | 0.8746 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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Model tree for Dulfary/platzi-distilroberta-base-mrpc-glue-prueba
Base model
distilbert/distilroberta-base