rmtariq/ft-Malay-bert

Model Performance

Classification Report

Class	Precision	Recall	F1-Score	Support
Negative	0.90	0.89	0.90	14692
Neutral	0.90	0.88	0.89	16970
Positive	0.89	0.92	0.90	16861

• Accuracy: 90%
• Macro Avg Precision: 0.90
• Macro Avg Recall: 0.90
• Macro Avg F1-Score: 0.90

Summary

This model achieves a balanced performance across all sentiment classes, with high precision and recall, especially in the positive and negative classes.

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tags: - text-classification - sentiment-analysis pipeline_tag: text-classification

Model Name

ft-Malay-bert

Model Description

This model is a fine-tuned version of BERT for the Malay language. It has been trained on [describe your dataset] to perform [specific task, e.g., sentiment analysis, classification, etc.].

Intended Uses & Limitations

This model is intended for [describe intended uses]. It should be noted that [mention any limitations or biases].

How to Use

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("rmtariq/ft-Malay-bert")
tokenizer = AutoTokenizer.from_pretrained("rmtariq/ft-Malay-bert")

inputs = tokenizer("Your text here", return_tensors="pt")
outputs = model(**inputs)