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.
Abbreviation | Description |
---|---|
O | Outside of a named entity |
B-LOC | Beginning of a location right after another location |
I-LOC | Location |
B-DAT | Beginning of a date right after another date |
I-DAT | Date |
B-TIM | Beginning of a time right after another time |
I-TIM | Time |
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
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1394 | 1.0 | 827 | 0.0559 | 0.8808 | 0.9257 | 0.9027 | 0.9842 |
0.0468 | 2.0 | 1654 | 0.0575 | 0.9107 | 0.9190 | 0.9148 | 0.9849 |
0.0279 | 3.0 | 2481 | 0.0567 | 0.9189 | 0.9273 | 0.9231 | 0.9859 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for rollerhafeezh-amikom/xlm-roberta-base-ner-silvanus
Base model
FacebookAI/xlm-roberta-baseEvaluation results
- Precision on id_nergrit_corpusvalidation set self-reported0.919
- Recall on id_nergrit_corpusvalidation set self-reported0.927
- F1 on id_nergrit_corpusvalidation set self-reported0.923
- Accuracy on id_nergrit_corpusvalidation set self-reported0.986