Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use zuriati/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zuriati/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zuriati/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zuriati/results") model = AutoModelForSequenceClassification.from_pretrained("zuriati/results") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of j-hartmann/emotion-english-distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3514
- Accuracy: 0.9365
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0542 | 1.0 | 1000 | 0.3514 | 0.9365 |
| 0.0515 | 2.0 | 2000 | 0.3438 | 0.935 |
| 0.0825 | 3.0 | 3000 | 0.3766 | 0.9315 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
- Downloads last month
- 4