---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_xlm-roberta-large-finetuned-conll03
  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: 0.9244064245810056
    - name: Recall
      type: recall
      value: 0.9375
    - name: F1
      type: f1
      value: 0.9309071729957805
    - name: Accuracy
      type: accuracy
      value: 0.9856142995585226
---

<!-- 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. -->

# my_xlm-roberta-large-finetuned-conll03

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: 0.1185
- Precision: 0.9244
- Recall: 0.9375
- F1: 0.9309
- Accuracy: 0.9856

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1533        | 1.0   | 878  | 0.1178          | 0.8950    | 0.9053 | 0.9001 | 0.9805   |
| 0.0303        | 2.0   | 1756 | 0.1157          | 0.9157    | 0.9331 | 0.9243 | 0.9843   |
| 0.0164        | 3.0   | 2634 | 0.1185          | 0.9244    | 0.9375 | 0.9309 | 0.9856   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1