neel-jotaniya/NER-advance-2.0

NER-Advance

This model is a fine-tuned version of 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