---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1592
- Precision: 0.7852
- Recall: 0.8012
- F1: 0.7931
- Accuracy: 0.9701

## 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: 8e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 131  | 0.1607          | 0.6254    | 0.6801 | 0.6516 | 0.9538   |
| No log        | 2.0   | 262  | 0.1188          | 0.7437    | 0.7695 | 0.7564 | 0.9670   |
| No log        | 3.0   | 393  | 0.1264          | 0.7556    | 0.7750 | 0.7652 | 0.9675   |
| 0.0923        | 4.0   | 524  | 0.1344          | 0.7622    | 0.7858 | 0.7738 | 0.9680   |
| 0.0923        | 5.0   | 655  | 0.1442          | 0.7741    | 0.7835 | 0.7788 | 0.9694   |
| 0.0923        | 6.0   | 786  | 0.1501          | 0.7892    | 0.8104 | 0.7997 | 0.9703   |
| 0.0923        | 7.0   | 917  | 0.1584          | 0.7750    | 0.7964 | 0.7856 | 0.9694   |
| 0.0133        | 8.0   | 1048 | 0.1592          | 0.7852    | 0.8012 | 0.7931 | 0.9701   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3