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---
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
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