bert-tagalog-base-uncased-ner-v1

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Description

• The dataset consists of 148 Filipino storytelling books, 5,005 total sentences, 45,792 total tokens, and 5,646 unique tokens.
• This NER model only supports the Filipino language and does not include proper nouns, verbs, adjectives, and adverbs as of the moment
• The input must undergo preprocessing. Soon I will upload the code to GitHub for preprocessing the input
• To replicate the preprocessed input use this example as a guide
• Input: "May umaapoy na bahay "
• Preprocessed Input: "apoy bahay"

bert-tagalog-base-uncased-ner-v1

This model is a fine-tuned version of jcblaise/bert-tagalog-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2824
• Precision: 0.9091
• Recall: 0.8988
• F1: 0.9039
• Accuracy: 0.9488

Model description

More information needed

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.24.0
• Pytorch 1.12.1+cu113
• Datasets 2.7.1
• Tokenizers 0.13.2