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---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-RoBERTa-Large-Conll2003-English-NER-Finetune
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9247648902821317
- name: Recall
type: recall
value: 0.9401558073654391
- name: F1
type: f1
value: 0.932396839332748
- name: Accuracy
type: accuracy
value: 0.9851405190050608
---
<!-- 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. -->
# XLM-RoBERTa-Large-Conll2003-English-NER-Finetune
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2205
- Precision: 0.9248
- Recall: 0.9402
- F1: 0.9324
- Accuracy: 0.9851
## 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6457 | 0.3333 | 1441 | 0.1841 | 0.8167 | 0.8336 | 0.8250 | 0.9674 |
| 0.0893 | 0.6667 | 2882 | 0.1399 | 0.8827 | 0.8950 | 0.8888 | 0.9781 |
| 0.0637 | 1.0 | 4323 | 0.1449 | 0.8939 | 0.9024 | 0.8981 | 0.9802 |
| 0.0457 | 1.3333 | 5764 | 0.1552 | 0.8955 | 0.9163 | 0.9057 | 0.9816 |
| 0.0387 | 1.6667 | 7205 | 0.1566 | 0.9041 | 0.9233 | 0.9136 | 0.9825 |
| 0.0401 | 2.0 | 8646 | 0.1493 | 0.8982 | 0.9311 | 0.9144 | 0.9824 |
| 0.0276 | 2.3333 | 10087 | 0.1655 | 0.9038 | 0.9299 | 0.9167 | 0.9820 |
| 0.0248 | 2.6667 | 11528 | 0.1783 | 0.9127 | 0.9309 | 0.9217 | 0.9829 |
| 0.0266 | 3.0 | 12969 | 0.1601 | 0.9120 | 0.9340 | 0.9228 | 0.9833 |
| 0.0166 | 3.3333 | 14410 | 0.1801 | 0.9181 | 0.9288 | 0.9234 | 0.9842 |
| 0.0187 | 3.6667 | 15851 | 0.1717 | 0.9170 | 0.9325 | 0.9247 | 0.9843 |
| 0.0185 | 4.0 | 17292 | 0.1653 | 0.9190 | 0.9343 | 0.9266 | 0.9844 |
| 0.0126 | 4.3333 | 18733 | 0.1845 | 0.9176 | 0.9343 | 0.9259 | 0.9843 |
| 0.0133 | 4.6667 | 20174 | 0.1855 | 0.9174 | 0.9322 | 0.9247 | 0.9837 |
| 0.0119 | 5.0 | 21615 | 0.1782 | 0.9168 | 0.9329 | 0.9248 | 0.9843 |
| 0.01 | 5.3333 | 23056 | 0.1892 | 0.9173 | 0.9366 | 0.9269 | 0.9843 |
| 0.0083 | 5.6667 | 24497 | 0.1800 | 0.9251 | 0.9343 | 0.9297 | 0.9845 |
| 0.0079 | 6.0 | 25938 | 0.1868 | 0.9237 | 0.9352 | 0.9294 | 0.9851 |
| 0.0059 | 6.3333 | 27379 | 0.2073 | 0.9178 | 0.9350 | 0.9263 | 0.9842 |
| 0.0068 | 6.6667 | 28820 | 0.2061 | 0.9195 | 0.9379 | 0.9286 | 0.9843 |
| 0.0062 | 7.0 | 30261 | 0.2011 | 0.9215 | 0.9377 | 0.9295 | 0.9846 |
| 0.0037 | 7.3333 | 31702 | 0.2100 | 0.9209 | 0.9373 | 0.9290 | 0.9846 |
| 0.0043 | 7.6667 | 33143 | 0.2145 | 0.9202 | 0.9389 | 0.9295 | 0.9847 |
| 0.0039 | 8.0 | 34584 | 0.2070 | 0.9256 | 0.9377 | 0.9316 | 0.9852 |
| 0.0024 | 8.3333 | 36025 | 0.2138 | 0.9218 | 0.9394 | 0.9306 | 0.9851 |
| 0.0034 | 8.6667 | 37466 | 0.2159 | 0.9229 | 0.9394 | 0.9311 | 0.9849 |
| 0.003 | 9.0 | 38907 | 0.2156 | 0.9244 | 0.9377 | 0.9310 | 0.9846 |
| 0.002 | 9.3333 | 40348 | 0.2201 | 0.9252 | 0.9402 | 0.9326 | 0.9849 |
| 0.0015 | 9.6667 | 41789 | 0.2217 | 0.9245 | 0.9393 | 0.9318 | 0.9850 |
| 0.0028 | 10.0 | 43230 | 0.2205 | 0.9248 | 0.9402 | 0.9324 | 0.9851 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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