---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-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. -->

# distilbert-base-uncased-finetuned-ner-harem

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2794
- Precision: 0.6556
- Recall: 0.6324
- F1: 0.6438
- Accuracy: 0.9448

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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        | 1.0   | 282  | 0.3860          | 0.3575    | 0.2411 | 0.2880 | 0.9035   |
| 0.4189        | 2.0   | 564  | 0.3048          | 0.5051    | 0.4165 | 0.4566 | 0.9227   |
| 0.4189        | 3.0   | 846  | 0.2893          | 0.5924    | 0.5025 | 0.5438 | 0.9303   |
| 0.209         | 4.0   | 1128 | 0.2752          | 0.5640    | 0.5649 | 0.5644 | 0.9335   |
| 0.209         | 5.0   | 1410 | 0.2880          | 0.6466    | 0.5616 | 0.6011 | 0.9409   |
| 0.1252        | 6.0   | 1692 | 0.2656          | 0.6404    | 0.5885 | 0.6134 | 0.9426   |
| 0.1252        | 7.0   | 1974 | 0.2662          | 0.6367    | 0.6324 | 0.6345 | 0.9419   |
| 0.0859        | 8.0   | 2256 | 0.2717          | 0.6584    | 0.6273 | 0.6425 | 0.9444   |
| 0.0593        | 9.0   | 2538 | 0.2774          | 0.6590    | 0.6290 | 0.6437 | 0.9440   |
| 0.0593        | 10.0  | 2820 | 0.2794          | 0.6556    | 0.6324 | 0.6438 | 0.9448   |


### Framework versions

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