Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use VirginiaAchille/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VirginiaAchille/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VirginiaAchille/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VirginiaAchille/results") model = AutoModelForSequenceClassification.from_pretrained("VirginiaAchille/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of castorini/afriberta_large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4618
- Accuracy: 0.9177
- F1: 0.9176
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: 16
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 122 | 0.4023 | 0.8848 | 0.8867 |
| No log | 2.0 | 244 | 0.4081 | 0.9136 | 0.9136 |
| No log | 3.0 | 366 | 0.4208 | 0.9177 | 0.9176 |
| No log | 4.0 | 488 | 0.4357 | 0.9177 | 0.9178 |
| 0.2252 | 5.0 | 610 | 0.4618 | 0.9177 | 0.9176 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Downloads last month
- 9
Model tree for VirginiaAchille/results
Base model
castorini/afriberta_large