Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use leonardosaveri/DSChallenge_Roberta_Base_Parameters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonardosaveri/DSChallenge_Roberta_Base_Parameters with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leonardosaveri/DSChallenge_Roberta_Base_Parameters")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leonardosaveri/DSChallenge_Roberta_Base_Parameters") model = AutoModelForSequenceClassification.from_pretrained("leonardosaveri/DSChallenge_Roberta_Base_Parameters", device_map="auto") - Notebooks
- Google Colab
- Kaggle
DSChallenge_Roberta_Base_Parameters
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3702
- Accuracy: 0.9392
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- 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 |
|---|---|---|---|---|
| 0.3735 | 1.0 | 3169 | 0.4367 | 0.9204 |
| 0.3029 | 2.0 | 6338 | 0.3719 | 0.9374 |
| 0.2616 | 3.0 | 9507 | 0.3662 | 0.9388 |
| 0.2785 | 4.0 | 12676 | 0.3702 | 0.9392 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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