# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Bharat2004/out")
model = AutoModelForSequenceClassification.from_pretrained("Bharat2004/out")Quick Links
out
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: 0.0508
- Accuracy: 0.9891
- F1 Macro: 0.9891
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 100
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.0955 | 1.0 | 585 | 0.0307 | 0.9873 | 0.9873 |
| 0.0432 | 2.0 | 1170 | 0.0357 | 0.9891 | 0.9891 |
| 0.0191 | 3.0 | 1755 | 0.0374 | 0.9885 | 0.9885 |
| 0.0082 | 4.0 | 2340 | 0.0415 | 0.9897 | 0.9897 |
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 Bharat2004/out
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bharat2004/out")