Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jefercania/distilroberta-base-mrpc-glue")
model = AutoModelForSequenceClassification.from_pretrained("jefercania/distilroberta-base-mrpc-glue")Quick Links
distilroberta-base-mrpc-glue
This model is a fine-tuned version of distilroberta-base on the datasetX dataset. It achieves the following results on the evaluation set:
- Loss: 0.4936
- Accuracy: 0.8382
- F1: 0.8893
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 |
|---|---|---|---|---|---|
| 0.5377 | 1.09 | 500 | 0.4936 | 0.8382 | 0.8893 |
| 0.3477 | 2.18 | 1000 | 0.6595 | 0.8407 | 0.8862 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for jefercania/distilroberta-base-mrpc-glue
Base model
distilbert/distilroberta-baseDataset used to train jefercania/distilroberta-base-mrpc-glue
Evaluation results
- Accuracy on datasetXvalidation set self-reported0.838
- F1 on datasetXvalidation set self-reported0.889
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jefercania/distilroberta-base-mrpc-glue")