---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-uncased-finetuned-ner-harem
  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-large-uncased-finetuned-ner-harem

This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co./google-bert/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3109
- Precision: 0.6895
- Recall: 0.6442
- F1: 0.6661
- Accuracy: 0.9512

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9978 | 281  | 0.2896          | 0.5442    | 0.4772 | 0.5085 | 0.9238   |
| 0.3496        | 1.9973 | 562  | 0.2340          | 0.6811    | 0.5295 | 0.5958 | 0.9412   |
| 0.3496        | 2.9969 | 843  | 0.2240          | 0.5876    | 0.5599 | 0.5734 | 0.9409   |
| 0.1372        | 3.9964 | 1124 | 0.2540          | 0.6910    | 0.6223 | 0.6548 | 0.9403   |
| 0.1372        | 4.9960 | 1405 | 0.2598          | 0.6433    | 0.6358 | 0.6395 | 0.9439   |
| 0.0648        | 5.9956 | 1686 | 0.2377          | 0.6945    | 0.6442 | 0.6684 | 0.9497   |
| 0.0648        | 6.9951 | 1967 | 0.2822          | 0.6965    | 0.6425 | 0.6684 | 0.9501   |
| 0.0316        | 7.9982 | 2249 | 0.2958          | 0.7044    | 0.6509 | 0.6766 | 0.9518   |
| 0.0148        | 8.9978 | 2530 | 0.3006          | 0.6944    | 0.6476 | 0.6702 | 0.9496   |
| 0.0148        | 9.9938 | 2810 | 0.3109          | 0.6895    | 0.6442 | 0.6661 | 0.9512   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3