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---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- shipping_label_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_roberta_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: 0.5272727272727272
    - name: Recall
      type: recall
      value: 0.7837837837837838
    - name: F1
      type: f1
      value: 0.6304347826086956
    - name: Accuracy
      type: accuracy
      value: 0.7796610169491526
---

<!-- 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_roberta_model

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the shipping_label_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0623
- Precision: 0.5273
- Recall: 0.7838
- F1: 0.6304
- Accuracy: 0.7797

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 14   | 1.1206          | 0.3125    | 0.4054 | 0.3529 | 0.6610   |
| No log        | 2.0   | 28   | 0.7363          | 0.5128    | 0.5405 | 0.5263 | 0.7119   |
| No log        | 3.0   | 42   | 0.6219          | 0.5333    | 0.6486 | 0.5854 | 0.7542   |
| No log        | 4.0   | 56   | 0.7328          | 0.4727    | 0.7027 | 0.5652 | 0.7627   |
| No log        | 5.0   | 70   | 0.8181          | 0.5       | 0.7297 | 0.5934 | 0.7542   |
| No log        | 6.0   | 84   | 0.8485          | 0.5185    | 0.7568 | 0.6154 | 0.7627   |
| No log        | 7.0   | 98   | 0.9692          | 0.5       | 0.7027 | 0.5843 | 0.7542   |
| No log        | 8.0   | 112  | 0.9842          | 0.4915    | 0.7838 | 0.6042 | 0.7458   |
| No log        | 9.0   | 126  | 1.1196          | 0.5       | 0.7838 | 0.6105 | 0.7542   |
| No log        | 10.0  | 140  | 1.2147          | 0.5       | 0.7838 | 0.6105 | 0.7542   |
| No log        | 11.0  | 154  | 1.4110          | 0.5       | 0.7568 | 0.6022 | 0.7712   |
| No log        | 12.0  | 168  | 1.2104          | 0.5370    | 0.7838 | 0.6374 | 0.7881   |
| No log        | 13.0  | 182  | 1.4145          | 0.5283    | 0.7568 | 0.6222 | 0.7797   |
| No log        | 14.0  | 196  | 1.4939          | 0.5179    | 0.7838 | 0.6237 | 0.7712   |
| No log        | 15.0  | 210  | 1.5558          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |
| No log        | 16.0  | 224  | 1.5639          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |
| No log        | 17.0  | 238  | 1.5208          | 0.5179    | 0.7838 | 0.6237 | 0.7712   |
| No log        | 18.0  | 252  | 1.4787          | 0.5918    | 0.7838 | 0.6744 | 0.7966   |
| No log        | 19.0  | 266  | 1.3946          | 0.5283    | 0.7568 | 0.6222 | 0.7797   |
| No log        | 20.0  | 280  | 1.6672          | 0.5370    | 0.7838 | 0.6374 | 0.7881   |
| No log        | 21.0  | 294  | 1.5746          | 0.5185    | 0.7568 | 0.6154 | 0.7712   |
| No log        | 22.0  | 308  | 1.8881          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| No log        | 23.0  | 322  | 1.5084          | 0.5370    | 0.7838 | 0.6374 | 0.7881   |
| No log        | 24.0  | 336  | 1.7922          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| No log        | 25.0  | 350  | 1.7265          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |
| No log        | 26.0  | 364  | 1.7467          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |
| No log        | 27.0  | 378  | 2.0162          | 0.5       | 0.7568 | 0.6022 | 0.7627   |
| No log        | 28.0  | 392  | 1.9460          | 0.5       | 0.7568 | 0.6022 | 0.7627   |
| No log        | 29.0  | 406  | 1.8957          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| No log        | 30.0  | 420  | 1.9941          | 0.5       | 0.7568 | 0.6022 | 0.7627   |
| No log        | 31.0  | 434  | 1.9095          | 0.5       | 0.7568 | 0.6022 | 0.7712   |
| No log        | 32.0  | 448  | 1.8920          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |
| No log        | 33.0  | 462  | 1.9310          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| No log        | 34.0  | 476  | 1.9830          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| No log        | 35.0  | 490  | 2.0445          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 36.0  | 504  | 2.1138          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 37.0  | 518  | 2.0024          | 0.5091    | 0.7568 | 0.6087 | 0.7797   |
| 0.2599        | 38.0  | 532  | 2.0004          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 39.0  | 546  | 2.0725          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 40.0  | 560  | 2.0507          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 41.0  | 574  | 2.0548          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 42.0  | 588  | 2.1176          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 43.0  | 602  | 2.0946          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 44.0  | 616  | 2.1211          | 0.5       | 0.7568 | 0.6022 | 0.7627   |
| 0.2599        | 45.0  | 630  | 2.1103          | 0.5091    | 0.7568 | 0.6087 | 0.7712   |
| 0.2599        | 46.0  | 644  | 2.0876          | 0.5       | 0.7568 | 0.6022 | 0.7627   |
| 0.2599        | 47.0  | 658  | 2.0910          | 0.5179    | 0.7838 | 0.6237 | 0.7712   |
| 0.2599        | 48.0  | 672  | 2.0800          | 0.5179    | 0.7838 | 0.6237 | 0.7712   |
| 0.2599        | 49.0  | 686  | 2.0584          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |
| 0.2599        | 50.0  | 700  | 2.0623          | 0.5273    | 0.7838 | 0.6304 | 0.7797   |


### Framework versions

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