Create rule-based/6-rb.txt

Upload 4 files (#44)

Upload 4 files (#43)

Upload 4 files (#42)

Upload 4 files (#41)

Create Training set/LLM (gemini)/6-llm.txt (#40)

Create Test set/6-ts.txt (#39)

Create Rule-based trained Gemma Model/rule-based/6-sp.txt (#38)

Upload 6 files (#37)

Upload 2 files (#36)

Upload 2 files (#35)

Upload Test Dataset.csv (#34)

Create Version 2 (Gemini Fine-Tune Model)/6-g.txt (#33)

Create Specific Models/LLM trained Gemma Model/gemini/6-g.txt (#32)

Create Hierarchical Inference/Test set/6-2.txt (#31)

Create General Models/Version 1 (Rule-Based Train Dataset)/6-1.txt (#30)

Create General Models/Version 1 (Rule-Based Train Dataset)/6-1.txt (#30)

Upload 4 files (#29)

Upload 4 files (#28)

Upload 4 files (#27)

Create truth/4-tr.txt (#26)

Create test-set/4-ts.txt (#25)

Upload 3 files (#24)

Upload 4 files (#23)

Upload 4 files (#22)

Upload 4 files (#21)

Create specific-data/examples/4-ex.txt (#20)

Create truth/4-t.txt (#19)

Create test-sets/4-ts.txt (#18)

Upload extractor_validation.py (#17)

Create aspect-identification/general-data/4-gd.txt (#16)

Upload Rule-Based Keyword Annotator.ipynb (#15)

Create aspect-extraction/data/examples/4-1.txt (#14)

Upload 5 files (#13)

Upload 2 files (#12)

Upload 3 files (#11)

Upload preprocess.py (#10)

Upload SentiTaglish_ProductsAndServices.csv (#9)

Create 6 _ Fine-Tuning (Gemma)/6.txt (#8)

Create 5 _ Rule-Based/5.txt (#7)

Create 4 _ LLM (Gemini)/4.txt (#6)

Create 3 _ Data Annotation/3.txt (#5)

Create 2 _ Topic Modeling/2.txt (#4)

Create 1 _ Data Preprocessing/1.txt (#3)

Create 0 _ Dataset Used/0.txt (#2)

initial commit