Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use ericNguyen0132/DepRoBERTa-2ndStage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ericNguyen0132/DepRoBERTa-2ndStage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ericNguyen0132/DepRoBERTa-2ndStage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ericNguyen0132/DepRoBERTa-2ndStage") model = AutoModelForSequenceClassification.from_pretrained("ericNguyen0132/DepRoBERTa-2ndStage") - Notebooks
- Google Colab
- Kaggle
DepRoBERTa-2ndStage
This model is a fine-tuned version of rafalposwiata/deproberta-large-v1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6330
- Accuracy: 0.855
- F1: 0.9134
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-06
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 469 | 0.3572 | 0.8617 | 0.9224 |
| 0.4953 | 2.0 | 938 | 0.3593 | 0.8783 | 0.9315 |
| 0.3493 | 3.0 | 1407 | 0.4274 | 0.8483 | 0.9091 |
| 0.313 | 4.0 | 1876 | 0.5488 | 0.8617 | 0.9187 |
| 0.2622 | 5.0 | 2345 | 0.6330 | 0.855 | 0.9134 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
- 5