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library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: bert-new-ner
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-new-ner
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0246
- Precision: 0.9645
- Recall: 0.9682
- F1: 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.0227 | 1.0 | 1002 | 0.0263 | 0.9540 | 0.9614 | 0.9577 |
| 0.0125 | 2.0 | 2004 | 0.0237 | 0.9554 | 0.9720 | 0.9637 |
| 0.0064 | 3.0 | 3006 | 0.0246 | 0.9645 | 0.9682 | 0.9664 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4
|