# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("OliverHeine/bert-large-uncased_fold_4")
model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/bert-large-uncased_fold_4")Quick Links
bert-large-uncased_fold_4
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0432
- Accuracy: 0.9940
- F1: 0.9888
- Precision: 0.9966
- Recall: 0.9811
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: 15
- eval_batch_size: 15
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0551 | 1.0 | 10089 | 0.0432 | 0.9923 | 0.9855 | 0.9951 | 0.9762 |
| 0.0188 | 2.0 | 20178 | 0.0406 | 0.9937 | 0.9882 | 0.9958 | 0.9808 |
| 0.0083 | 3.0 | 30267 | 0.0432 | 0.9940 | 0.9888 | 0.9966 | 0.9811 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for OliverHeine/bert-large-uncased_fold_4
Base model
google-bert/bert-large-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/bert-large-uncased_fold_4")