---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-Conll2003-ner-2024_08_05
  results: []
---

<!-- 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-base-finetuned-Conll2003-ner-2024_08_05

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1404
- Precision: 0.9004
- Recall: 0.9163
- F1: 0.9083
- Accuracy: 0.9780

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0838        | 0.3326 | 292  | 0.1220          | 0.8779    | 0.8841 | 0.8810 | 0.9730   |
| 0.0807        | 0.6651 | 584  | 0.1345          | 0.8695    | 0.8934 | 0.8813 | 0.9728   |
| 0.0711        | 0.9977 | 876  | 0.1336          | 0.8728    | 0.8986 | 0.8855 | 0.9733   |
| 0.0467        | 1.3303 | 1168 | 0.1443          | 0.8817    | 0.9090 | 0.8951 | 0.9748   |
| 0.0452        | 1.6629 | 1460 | 0.1311          | 0.8887    | 0.9138 | 0.9011 | 0.9759   |
| 0.0383        | 1.9954 | 1752 | 0.1324          | 0.9021    | 0.9146 | 0.9083 | 0.9776   |
| 0.026         | 2.3280 | 2044 | 0.1352          | 0.9024    | 0.9180 | 0.9101 | 0.9784   |
| 0.0245        | 2.6606 | 2336 | 0.1431          | 0.9010    | 0.9172 | 0.9090 | 0.9778   |
| 0.0235        | 2.9932 | 2628 | 0.1403          | 0.9004    | 0.9163 | 0.9083 | 0.9780   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1