Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("OliverHeine/distilbert-base-uncased_fold_1")
model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/distilbert-base-uncased_fold_1")Quick Links
distilbert-base-uncased_fold_1
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: 0.1173
- Accuracy: 0.9644
- F1: 0.9610
- Precision: 0.9670
- Recall: 0.9551
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: 40
- eval_batch_size: 40
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0986 | 1.0 | 15481 | 0.1133 | 0.9597 | 0.9559 | 0.9612 | 0.9506 |
| 0.0781 | 2.0 | 30962 | 0.1048 | 0.9634 | 0.9599 | 0.9652 | 0.9546 |
| 0.0539 | 3.0 | 46443 | 0.1173 | 0.9644 | 0.9610 | 0.9670 | 0.9551 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2
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Model tree for OliverHeine/distilbert-base-uncased_fold_1
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/distilbert-base-uncased_fold_1")