---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: NER-advance-2.0
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# NER-Advance

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an XTREME dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8658


## Training and evaluation data

I used subset of the Cross-lingual TRansfer Evaluation of
Multilingual Encoders (XTREME) benchmark called WikiANN or PAN-X.2 This
dataset consists of Wikipedia articles in many languages, including the four most
commonly spoken languages in Switzerland: German (62.9%), French (22.9%), Ital‐
ian (8.4%), and English (5.9%). Each article is annotated with LOC (location), PER
(person), and ORG (organization) tags in the “inside-outside-beginning” (IOB2) for‐
mat. In this format, a B- prefix indicates the beginning of an entity, and consecutive
tokens belonging to the same entity are given an I- prefix.


### 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539        | 1.0   | 525  | 0.1505          | 0.8246 |
| 0.1268        | 2.0   | 1050 | 0.1380          | 0.8503 |
| 0.0794        | 3.0   | 1575 | 0.1363          | 0.8658 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2