Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("FareehaAly/fator-argument-quality")
model = AutoModelForSequenceClassification.from_pretrained("FareehaAly/fator-argument-quality")Quick Links
fator-argument-quality
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5797
- Accuracy: 0.8014
- F1 Macro: 0.5806
- F1 Weighted: 0.7459
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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|
| 0.5548 | 1.0 | 558 | 0.5412 | 0.7741 | 0.3669 | 0.7188 |
| 0.4892 | 2.0 | 1116 | 0.5830 | 0.7866 | 0.3644 | 0.7055 |
| 0.4455 | 3.0 | 1674 | 0.5797 | 0.7814 | 0.4606 | 0.7449 |
| 0.3561 | 4.0 | 2232 | 0.6330 | 0.7902 | 0.4996 | 0.7457 |
| 0.3084 | 5.0 | 2790 | 0.6744 | 0.8057 | 0.5806 | 0.7459 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for FareehaAly/fator-argument-quality
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FareehaAly/fator-argument-quality")