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---
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
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