Bert Base Uncased Fold 0
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: 0.0296 Accuracy: 0.9947 F1: 0.9902 Precision: 0.9971 Recall: 0.9833
Fonte do modelo
- Tipo
- Embedding
- Tamanho do arquivo
- 418 MB
- Modelo base
- bert-base-uncased
- Versão
- vRevision 397fc48
- Atualizado
- 1 de out. de 2026
- Publicador
- @OliverHeine
- Downloads
- 15
- Fonte
- Hugging Face
Descrição da fonte
bert-base-uncased_fold_0
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: 0.0296
- Accuracy: 0.9947
- F1: 0.9902
- Precision: 0.9971
- Recall: 0.9833
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 |
|---|---|---|---|---|---|---|---|
| 0.0281 | 1.0 | 3784 | 0.0311 | 0.9926 | 0.9861 | 0.9975 | 0.975 |
| 0.0086 | 2.0 | 7568 | 0.0300 | 0.9943 | 0.9895 | 0.9982 | 0.9809 |
| 0.0223 | 3.0 | 11352 | 0.0296 | 0.9947 | 0.9902 | 0.9971 | 0.9833 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2
Fontes
1 fonte
Fontes
1 fonteVerificado 1 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: bert-base-uncased_fold_0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased_fold_0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0296 - Accuracy: 0.9947 - F1: 0.9902 - Precision: 0.9971 - Recall: 0.9833 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.0281 | 1.0 | 3784 | 0.0311 | 0.9926 | 0.9861 | 0.9975 | 0.975 | | 0.0086 | 2.0 | 7568 | 0.0300 | 0.9943 | 0.9895 | 0.9982 | 0.9809 | | 0.0223 | 3.0 | 11352 | 0.0296 | 0.9947 | 0.9902 | 0.9971 | 0.9833 | ### Framework versions - Transformers 5.3.0 - Pytorch 2.10.0+cu128 - Datasets 4.6.1 - Tokenizers 0.22.2
Source context: 15 downloads · 0 likes · Pipeline text-classification · Library transformers · Repo OliverHeine/bert-base-uncased_fold_0
Source context: 0 downloads · 0 likes · Pipeline text-classification · Library transformers · Repo OliverHeine/bert-base-uncased_fold_0
