---
license: apache-2.0
base_model: distilbert-base-cased
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: 0.5178571428571429
    - name: Recall
      type: recall
      value: 0.7837837837837838
    - name: F1
      type: f1
      value: 0.6236559139784947
    - name: Accuracy
      type: accuracy
      value: 0.7796610169491526
---

<!-- 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 [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the shipping_label_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7118
- Precision: 0.5179
- Recall: 0.7838
- F1: 0.6237
- Accuracy: 0.7797

## 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    | 1.8106          | 0.0       | 0.0    | 0.0    | 0.5169   |
| No log        | 2.0   | 14   | 1.6175          | 0.5556    | 0.1351 | 0.2174 | 0.5932   |
| No log        | 3.0   | 21   | 1.3124          | 0.6       | 0.2432 | 0.3462 | 0.6441   |
| No log        | 4.0   | 28   | 1.1318          | 0.6471    | 0.5946 | 0.6197 | 0.8051   |
| No log        | 5.0   | 35   | 0.9306          | 0.6176    | 0.5676 | 0.5915 | 0.7881   |
| No log        | 6.0   | 42   | 0.8279          | 0.5476    | 0.6216 | 0.5823 | 0.7712   |
| No log        | 7.0   | 49   | 0.7609          | 0.5952    | 0.6757 | 0.6329 | 0.7881   |
| No log        | 8.0   | 56   | 0.7484          | 0.6327    | 0.8378 | 0.7209 | 0.8220   |
| No log        | 9.0   | 63   | 0.7035          | 0.6596    | 0.8378 | 0.7381 | 0.8220   |
| No log        | 10.0  | 70   | 0.7281          | 0.5741    | 0.8378 | 0.6813 | 0.7881   |
| No log        | 11.0  | 77   | 0.6970          | 0.5741    | 0.8378 | 0.6813 | 0.7881   |
| No log        | 12.0  | 84   | 0.6790          | 0.5       | 0.7568 | 0.6022 | 0.7881   |
| No log        | 13.0  | 91   | 0.7124          | 0.4828    | 0.7568 | 0.5895 | 0.7712   |
| No log        | 14.0  | 98   | 0.6770          | 0.5       | 0.7568 | 0.6022 | 0.7797   |
| No log        | 15.0  | 105  | 0.7219          | 0.5179    | 0.7838 | 0.6237 | 0.7797   |
| No log        | 16.0  | 112  | 0.6695          | 0.5273    | 0.7838 | 0.6304 | 0.7881   |
| No log        | 17.0  | 119  | 0.6885          | 0.5179    | 0.7838 | 0.6237 | 0.7797   |
| No log        | 18.0  | 126  | 0.7138          | 0.5088    | 0.7838 | 0.6170 | 0.7712   |
| No log        | 19.0  | 133  | 0.7113          | 0.5179    | 0.7838 | 0.6237 | 0.7797   |
| No log        | 20.0  | 140  | 0.7118          | 0.5179    | 0.7838 | 0.6237 | 0.7797   |


### Framework versions

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