Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use kevinvelez18/distilroberta-base-mrpc-glue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevinvelez18/distilroberta-base-mrpc-glue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kevinvelez18/distilroberta-base-mrpc-glue")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kevinvelez18/distilroberta-base-mrpc-glue") model = AutoModelForSequenceClassification.from_pretrained("kevinvelez18/distilroberta-base-mrpc-glue") - Notebooks
- Google Colab
- Kaggle
distilroberta-base-mrpc-glue
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: 0.9245
- Accuracy: 0.8333
- F1: 0.8828
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.3364 | 1.0893 | 500 | 0.9526 | 0.7966 | 0.8449 |
| 0.2097 | 2.1786 | 1000 | 0.9513 | 0.8235 | 0.8737 |
| 0.1005 | 3.2680 | 1500 | 0.9245 | 0.8333 | 0.8828 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for kevinvelez18/distilroberta-base-mrpc-glue
Base model
distilbert/distilroberta-base