Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("chrisnb1/distilroberta-base")
model = AutoModelForSequenceClassification.from_pretrained("chrisnb1/distilroberta-base")Quick Links
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: 0.5023
- Accuracy: 0.8284
- F1: 0.8741
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 |
|---|---|---|---|---|---|
| 0.5429 | 1.09 | 500 | 0.5987 | 0.7794 | 0.8295 |
| 0.384 | 2.18 | 1000 | 0.5023 | 0.8284 | 0.8741 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
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Dataset used to train chrisnb1/distilroberta-base
Evaluation results
- Accuracy on gluevalidation set self-reported0.828
- F1 on gluevalidation set self-reported0.874
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chrisnb1/distilroberta-base")