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---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8637881274404392
---
<!-- 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-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1569
- F1: 0.8638
## Model description
Multilingual Named Entity Recognition across several languages
For this project's token classification, I built a unique custom model head and the
WikiANN or PAN-X.2, which is a subset of the Cross-lingual TRansfer Evaluation of Multilingual
Encoders (XTREME) benchmark, was applied. This project was completed for a customer based
in switzerland, where the four languages that are most frequently spoken are
German (62.9% of articles), French (22.9%), Italian (8.4%), and English (5.9%).
## 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-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3044 | 1.0 | 525 | 0.1598 | 0.8174 |
| 0.1462 | 2.0 | 1050 | 0.1527 | 0.8308 |
| 0.1006 | 3.0 | 1575 | 0.1487 | 0.8459 |
| 0.0698 | 4.0 | 2100 | 0.1431 | 0.8615 |
| 0.0472 | 5.0 | 2625 | 0.1569 | 0.8638 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2 |