---
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: 0.8095238095238095
    - name: Recall
      type: recall
      value: 0.9066666666666666
    - name: F1
      type: f1
      value: 0.8553459119496856
    - name: Accuracy
      type: accuracy
      value: 0.8926553672316384
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 0.4675
- Precision: 0.8095
- Recall: 0.9067
- F1: 0.8553
- Accuracy: 0.8927

## 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    | 1.9550          | 0.0       | 0.0    | 0.0    | 0.4294   |
| No log        | 2.0   | 14   | 1.7431          | 0.0       | 0.0    | 0.0    | 0.4407   |
| No log        | 3.0   | 21   | 1.5315          | 0.2632    | 0.0667 | 0.1064 | 0.5198   |
| No log        | 4.0   | 28   | 1.3289          | 0.5490    | 0.3733 | 0.4444 | 0.6215   |
| No log        | 5.0   | 35   | 1.1498          | 0.5246    | 0.4267 | 0.4706 | 0.6497   |
| No log        | 6.0   | 42   | 1.0278          | 0.5921    | 0.6    | 0.5960 | 0.7175   |
| No log        | 7.0   | 49   | 0.8915          | 0.6579    | 0.6667 | 0.6623 | 0.7684   |
| No log        | 8.0   | 56   | 0.8158          | 0.6786    | 0.76   | 0.7170 | 0.8023   |
| No log        | 9.0   | 63   | 0.7012          | 0.7342    | 0.7733 | 0.7532 | 0.8249   |
| No log        | 10.0  | 70   | 0.6421          | 0.7590    | 0.84   | 0.7975 | 0.8475   |
| No log        | 11.0  | 77   | 0.5944          | 0.8025    | 0.8667 | 0.8333 | 0.8757   |
| No log        | 12.0  | 84   | 0.5570          | 0.7976    | 0.8933 | 0.8428 | 0.8870   |
| No log        | 13.0  | 91   | 0.5088          | 0.8148    | 0.88   | 0.8462 | 0.8927   |
| No log        | 14.0  | 98   | 0.5156          | 0.8193    | 0.9067 | 0.8608 | 0.8983   |
| No log        | 15.0  | 105  | 0.4958          | 0.8171    | 0.8933 | 0.8535 | 0.8927   |
| No log        | 16.0  | 112  | 0.4646          | 0.8171    | 0.8933 | 0.8535 | 0.8927   |
| No log        | 17.0  | 119  | 0.4745          | 0.8095    | 0.9067 | 0.8553 | 0.8927   |
| No log        | 18.0  | 126  | 0.4749          | 0.8095    | 0.9067 | 0.8553 | 0.8927   |
| No log        | 19.0  | 133  | 0.4720          | 0.8095    | 0.9067 | 0.8553 | 0.8927   |
| No log        | 20.0  | 140  | 0.4675          | 0.8095    | 0.9067 | 0.8553 | 0.8927   |


### Framework versions

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