---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- shipping_label_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_bert_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: shipping_label_ner
      type: shipping_label_ner
      config: shipping_label_ner
      split: validation
      args: shipping_label_ner
    metrics:
    - name: Precision
      type: precision
      value: 0.8235294117647058
    - name: Recall
      type: recall
      value: 0.9333333333333333
    - name: F1
      type: f1
      value: 0.8749999999999999
    - name: Accuracy
      type: accuracy
      value: 0.9096045197740112
---

<!-- 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. -->

# ner_bert_model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the shipping_label_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4145
- Precision: 0.8235
- Recall: 0.9333
- F1: 0.8750
- Accuracy: 0.9096

## 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: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 7    | 1.7796          | 0.0       | 0.0    | 0.0    | 0.4294   |
| No log        | 2.0   | 14   | 1.4530          | 0.5       | 0.2667 | 0.3478 | 0.5650   |
| No log        | 3.0   | 21   | 1.1854          | 0.5510    | 0.36   | 0.4355 | 0.6384   |
| No log        | 4.0   | 28   | 0.9850          | 0.6667    | 0.5867 | 0.6241 | 0.7345   |
| No log        | 5.0   | 35   | 0.8189          | 0.6622    | 0.6533 | 0.6577 | 0.7797   |
| No log        | 6.0   | 42   | 0.7194          | 0.6914    | 0.7467 | 0.7179 | 0.8192   |
| No log        | 7.0   | 49   | 0.6126          | 0.7262    | 0.8133 | 0.7673 | 0.8588   |
| No log        | 8.0   | 56   | 0.5760          | 0.75      | 0.88   | 0.8098 | 0.8701   |
| No log        | 9.0   | 63   | 0.4819          | 0.8       | 0.9067 | 0.8500 | 0.8927   |
| No log        | 10.0  | 70   | 0.4610          | 0.7907    | 0.9067 | 0.8447 | 0.8983   |
| No log        | 11.0  | 77   | 0.4471          | 0.8       | 0.9067 | 0.8500 | 0.8927   |
| No log        | 12.0  | 84   | 0.4203          | 0.7931    | 0.92   | 0.8519 | 0.9040   |
| No log        | 13.0  | 91   | 0.4281          | 0.8256    | 0.9467 | 0.8820 | 0.9153   |
| No log        | 14.0  | 98   | 0.3913          | 0.8256    | 0.9467 | 0.8820 | 0.9153   |
| No log        | 15.0  | 105  | 0.3966          | 0.8235    | 0.9333 | 0.8750 | 0.9096   |
| No log        | 16.0  | 112  | 0.4033          | 0.8235    | 0.9333 | 0.8750 | 0.9096   |
| No log        | 17.0  | 119  | 0.4149          | 0.8140    | 0.9333 | 0.8696 | 0.9040   |
| No log        | 18.0  | 126  | 0.4150          | 0.8140    | 0.9333 | 0.8696 | 0.9040   |
| No log        | 19.0  | 133  | 0.4122          | 0.8235    | 0.9333 | 0.8750 | 0.9096   |
| No log        | 20.0  | 140  | 0.4145          | 0.8235    | 0.9333 | 0.8750 | 0.9096   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2