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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: 0.5178571428571429
- 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
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