---
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: 0.7627118644067796
---

<!-- 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: 1.2010
- Precision: 0.5179
- Recall: 0.7838
- F1: 0.6237
- Accuracy: 0.7627

## 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: 4
- 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   | 14   | 0.6828          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 2.0   | 28   | 0.8587          | 0.5273    | 0.7838 | 0.6304 | 0.7712   |
| No log        | 3.0   | 42   | 0.7206          | 0.5577    | 0.7838 | 0.6517 | 0.8136   |
| No log        | 4.0   | 56   | 0.8983          | 0.5370    | 0.7838 | 0.6374 | 0.7797   |
| No log        | 5.0   | 70   | 0.6964          | 0.5472    | 0.7838 | 0.6444 | 0.8051   |
| No log        | 6.0   | 84   | 0.9793          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 7.0   | 98   | 0.6047          | 0.5472    | 0.7838 | 0.6444 | 0.8051   |
| No log        | 8.0   | 112  | 1.0809          | 0.5179    | 0.7838 | 0.6237 | 0.7797   |
| No log        | 9.0   | 126  | 1.1726          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 10.0  | 140  | 1.0067          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 11.0  | 154  | 1.1439          | 0.5088    | 0.7838 | 0.6170 | 0.7627   |
| No log        | 12.0  | 168  | 0.8971          | 0.5370    | 0.7838 | 0.6374 | 0.7881   |
| No log        | 13.0  | 182  | 1.0603          | 0.5179    | 0.7838 | 0.6237 | 0.7542   |
| No log        | 14.0  | 196  | 1.2095          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 15.0  | 210  | 1.2395          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 16.0  | 224  | 1.2509          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 17.0  | 238  | 1.2317          | 0.5179    | 0.7838 | 0.6237 | 0.7542   |
| No log        | 18.0  | 252  | 1.2656          | 0.5179    | 0.7838 | 0.6237 | 0.7542   |
| No log        | 19.0  | 266  | 1.1950          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |
| No log        | 20.0  | 280  | 1.2010          | 0.5179    | 0.7838 | 0.6237 | 0.7627   |


### Framework versions

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