---
license: apache-2.0
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: 0.2495
- Precision: 0.9509
- Recall: 0.9509
- F1: 0.9509
- Accuracy: 0.9649

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1482        | 1.0   | 4261  | 0.3301          | 0.8673    | 0.8935 | 0.8802 | 0.9128   |
| 0.0901        | 2.0   | 8522  | 0.2701          | 0.9100    | 0.9186 | 0.9143 | 0.9468   |
| 0.0523        | 3.0   | 12783 | 0.2614          | 0.9252    | 0.9290 | 0.9271 | 0.9506   |
| 0.0392        | 4.0   | 17044 | 0.2636          | 0.9109    | 0.9280 | 0.9193 | 0.9447   |
| 0.0288        | 5.0   | 21305 | 0.2724          | 0.9276    | 0.9363 | 0.9319 | 0.9499   |
| 0.0189        | 6.0   | 25566 | 0.2642          | 0.9257    | 0.9363 | 0.9310 | 0.9570   |
| 0.0108        | 7.0   | 29827 | 0.2196          | 0.9437    | 0.9457 | 0.9447 | 0.9622   |
| 0.012         | 8.0   | 34088 | 0.2320          | 0.9447    | 0.9457 | 0.9452 | 0.9649   |
| 0.0044        | 9.0   | 38349 | 0.2859          | 0.9252    | 0.9426 | 0.9338 | 0.9594   |
| 0.0049        | 10.0  | 42610 | 0.2989          | 0.9344    | 0.9374 | 0.9359 | 0.9589   |
| 0.0034        | 11.0  | 46871 | 0.2862          | 0.9498    | 0.9478 | 0.9488 | 0.9618   |
| 0.0021        | 12.0  | 51132 | 0.2443          | 0.9530    | 0.9520 | 0.9525 | 0.9675   |
| 0.0027        | 13.0  | 55393 | 0.2549          | 0.95      | 0.9520 | 0.9510 | 0.9646   |
| 0.0018        | 14.0  | 59654 | 0.2439          | 0.9499    | 0.9499 | 0.9499 | 0.9663   |
| 0.0019        | 15.0  | 63915 | 0.2495          | 0.9509    | 0.9509 | 0.9509 | 0.9649   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3