---
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: 0.2870
- Precision: 0.6543
- Recall: 0.6256
- F1: 0.6397
- Accuracy: 0.9433

## 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  | 0.3862          | 0.4247    | 0.2901 | 0.3447 | 0.9041   |
| 0.4179        | 2.0   | 564  | 0.3053          | 0.5411    | 0.4216 | 0.4739 | 0.9228   |
| 0.4179        | 3.0   | 846  | 0.2923          | 0.6195    | 0.5025 | 0.5549 | 0.9315   |
| 0.2108        | 4.0   | 1128 | 0.2770          | 0.5641    | 0.5346 | 0.5489 | 0.9322   |
| 0.2108        | 5.0   | 1410 | 0.2789          | 0.6104    | 0.5548 | 0.5813 | 0.9369   |
| 0.1279        | 6.0   | 1692 | 0.2793          | 0.6171    | 0.5953 | 0.6060 | 0.9382   |
| 0.1279        | 7.0   | 1974 | 0.2790          | 0.6348    | 0.6037 | 0.6188 | 0.9417   |
| 0.0881        | 8.0   | 2256 | 0.2864          | 0.6490    | 0.6206 | 0.6345 | 0.9419   |
| 0.0615        | 9.0   | 2538 | 0.2874          | 0.6414    | 0.6003 | 0.6202 | 0.9417   |
| 0.0615        | 10.0  | 2820 | 0.2870          | 0.6543    | 0.6256 | 0.6397 | 0.9433   |


### Framework versions

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