xlm-roberta-base-ner-silvanus

<!-- 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-ner-silvanus

This model is a fine-tuned version of xlm-roberta-base on the Indonesian NER dataset. It achieves the following results on the evaluation set:

• Loss: 0.0567
• Precision: 0.9189
• Recall: 0.9273
• F1: 0.9231
• Accuracy: 0.9859

Model description

The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.

• Developed by: See associated paper
• Model type: Multi-lingual model
• Language(s) (NLP) or Countries (images): XLM-RoBERTa is a multilingual model trained on 100 different languages; see GitHub Repo for full list; model is fine-tuned on a dataset in English
• License: More information needed
• Related Models: RoBERTa, XLM
  • Parent Model: XLM-RoBERTa
• Resources for more information: GitHub Repo

Intended uses & limitations

This model can be used to extract multilingual information such as location, date and time on social media (Twitter, etc.). This model is limited by an Indonesian language training data set to be tested in 4 languages (English, Spanish, Italian and Slovak) using zero-shot transfer learning techniques to extract multilingual information.

Training and evaluation data

This model was fine-tuned on Indonesian NER datasets.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1