# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("OliverHeine/albert-base-v2_fold_1")
model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/albert-base-v2_fold_1")Quick Links
albert-base-v2_fold_1
This model is a fine-tuned version of albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1304
- Accuracy: 0.9617
- F1: 0.9582
- Precision: 0.9608
- Recall: 0.9556
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 |
|---|---|---|---|---|---|---|---|
| 0.1161 | 1.0 | 15481 | 0.1282 | 0.9531 | 0.9480 | 0.9651 | 0.9315 |
| 0.0869 | 2.0 | 30962 | 0.1121 | 0.9600 | 0.9563 | 0.9588 | 0.9538 |
| 0.0609 | 3.0 | 46443 | 0.1304 | 0.9617 | 0.9582 | 0.9608 | 0.9556 |
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/albert-base-v2_fold_1
Base model
albert/albert-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/albert-base-v2_fold_1")