---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  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

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.9345
- Recall: 0.9421
- F1: 0.9383
- Accuracy: 0.9852

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0303        | 1.0   | 878  | 0.0579          | 0.9295    | 0.9352 | 0.9324 | 0.9838   |
| 0.0167        | 2.0   | 1756 | 0.0619          | 0.9333    | 0.9421 | 0.9376 | 0.9849   |
| 0.0114        | 3.0   | 2634 | 0.0664          | 0.9345    | 0.9421 | 0.9383 | 0.9852   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.13.3