# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("OliverHeine/bert-base-uncased_fold_9")
model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/bert-base-uncased_fold_9")Quick Links
bert-base-uncased_fold_9
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0332
- Accuracy: 0.9951
- F1: 0.9909
- Precision: 0.9969
- Recall: 0.9850
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: 38
- eval_batch_size: 38
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0380 | 1.0 | 3983 | 0.0258 | 0.9939 | 0.9886 | 0.9971 | 0.9802 |
| 0.0156 | 2.0 | 7966 | 0.0248 | 0.9949 | 0.9906 | 0.9949 | 0.9863 |
| 0.0151 | 3.0 | 11949 | 0.0332 | 0.9951 | 0.9909 | 0.9969 | 0.9850 |
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/bert-base-uncased_fold_9
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/bert-base-uncased_fold_9")