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malay-sentiment-deberta-xsmall
malaysia-ai/malay-sentiment-deberta-xsmall
malay-sentiment-deberta-xsmall is a text classification model from malaysia-ai. Use it when you need a label for a piece of text. It is set up for transformers.
This model is a fine-tuned checkpoint of Deberta-V3-xsmall. It enables binary sentiment analysis for Malay-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. Model is trained…
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- deberta-v2
- Access
- Public
- Created
- Feb 13, 2024
- Updated
- Feb 13, 2024
- SHA
- 42f0851f
Model card
From the Hugging Face model README
Malay-Language Sentiment Classification
Overview
This model is a fine-tuned checkpoint of Deberta-V3-xsmall. It enables binary sentiment analysis for Malay-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. Model is trained on all data from https://github.com/mesolitica/malaysian-dataset/tree/master/sentiment.
Use in a Hugging Face pipeline
The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:
from transformers import pipeline
sentiment_analysis = pipeline("sentiment-analysis",model="malaysia-ai/deberta-v3-xsmall-malay-sentiment")
print(sentiment_analysis("saya comel"))
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- Languages
- ms
- Hf Inference
- live
- Created
- Feb 13, 2024
- Updated
- Feb 13, 2024