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

This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2665
- Precision: 0.7241
- Recall: 0.7423
- F1: 0.7331
- Accuracy: 0.9611

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9938 | 140  | 0.2609          | 0.5107    | 0.5626 | 0.5354 | 0.9324   |
| No log        | 1.9947 | 281  | 0.2057          | 0.6370    | 0.6642 | 0.6503 | 0.9517   |
| No log        | 2.9956 | 422  | 0.2106          | 0.6527    | 0.6642 | 0.6584 | 0.9566   |
| 0.2074        | 3.9965 | 563  | 0.2342          | 0.6843    | 0.7054 | 0.6947 | 0.9571   |
| 0.2074        | 4.9973 | 704  | 0.2369          | 0.7216    | 0.7290 | 0.7253 | 0.9614   |
| 0.2074        | 5.9982 | 845  | 0.2334          | 0.7013    | 0.7261 | 0.7135 | 0.9574   |
| 0.2074        | 6.9991 | 986  | 0.2580          | 0.7139    | 0.7570 | 0.7348 | 0.9592   |
| 0.0377        | 8.0    | 1127 | 0.2658          | 0.7452    | 0.7452 | 0.7452 | 0.9607   |
| 0.0377        | 8.9938 | 1267 | 0.2619          | 0.7543    | 0.7688 | 0.7615 | 0.9637   |
| 0.0377        | 9.9379 | 1400 | 0.2665          | 0.7241    | 0.7423 | 0.7331 | 0.9611   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1