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library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner2
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-cased-finetuned-ner2
This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1445
- Precision: 0.8221
- Recall: 0.8509
- F1: 0.8362
- Accuracy: 0.9656
## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1275 | 1.0 | 4750 | 0.1217 | 0.7861 | 0.8216 | 0.8034 | 0.9602 |
| 0.0985 | 2.0 | 9500 | 0.1209 | 0.8166 | 0.8266 | 0.8215 | 0.9630 |
| 0.0716 | 3.0 | 14250 | 0.1175 | 0.8209 | 0.8493 | 0.8349 | 0.9652 |
| 0.0448 | 4.0 | 19000 | 0.1360 | 0.8166 | 0.8470 | 0.8315 | 0.9652 |
| 0.037 | 5.0 | 23750 | 0.1445 | 0.8221 | 0.8509 | 0.8362 | 0.9656 |
### Framework versions
- Transformers 4.50.1
- Pytorch 2.5.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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