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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
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