transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer dataset:xtreme license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- xtreme
license: mit
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-panx-de
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: xtreme
      type: xtreme
      config: PAN-X.de
      split: validation
      args: PAN-X.de
    metrics:
    - type: f1
      value: 0.8637881274404392
      name: F1
---

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# 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