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---
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
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: 0.4675
- 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
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