metadata
library_name: peft
license: apache-2.0
base_model: bert-base-cased
tags:
- base_model:adapter:bert-base-cased
- lora
- transformers
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1173
- Precision: 0.7983
- Recall: 0.8684
- F1: 0.8319
- Accuracy: 0.9664
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: 8
- eval_batch_size: 8
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2226 | 1.0 | 1756 | 0.1639 | 0.7179 | 0.7972 | 0.7555 | 0.9512 |
| 0.1434 | 2.0 | 3512 | 0.1290 | 0.7881 | 0.8554 | 0.8204 | 0.9634 |
| 0.1344 | 3.0 | 5268 | 0.1173 | 0.7983 | 0.8684 | 0.8319 | 0.9664 |
Framework versions
- PEFT 0.18.1
- Transformers 4.57.6
- Pytorch 2.9.1
- Datasets 4.3.0
- Tokenizers 0.22.2