---
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