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("daliapv/new_model")
model = AutoModelForSequenceClassification.from_pretrained("daliapv/new_model")Quick Links
new_model
This model is a fine-tuned version of distilroberta-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6106
- Accuracy: 0.8211
- F1: 0.8620
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 |
|---|---|---|---|---|---|
| 0.5319 | 1.09 | 500 | 0.5276 | 0.8284 | 0.8822 |
| 0.3708 | 2.18 | 1000 | 0.6106 | 0.8211 | 0.8620 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
- 4
Dataset used to train daliapv/new_model
Evaluation results
- Accuracy on gluevalidation set self-reported0.821
- F1 on gluevalidation set self-reported0.862
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daliapv/new_model")