Instructions to use patrickxchong/bert-tiny-bahasa-cased-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickxchong/bert-tiny-bahasa-cased-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patrickxchong/bert-tiny-bahasa-cased-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patrickxchong/bert-tiny-bahasa-cased-sentiment") model = AutoModelForSequenceClassification.from_pretrained("patrickxchong/bert-tiny-bahasa-cased-sentiment") - Notebooks
- Google Colab
- Kaggle
bert-tiny-bahasa-cased-sentiment
Proof of concept of creating a sentiment analysis model with using https://huggingface.co/malay-huggingface/bert-base-bahasa-cased as the base model.
Tokenizer is copied directly from https://huggingface.co/malay-huggingface/bert-base-bahasa-cased.
Sentiment analysis fine tuning was done with data compiled by huseinzol05 at https://github.com/huseinzol05/malay-dataset/tree/master/sentiment.
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