albert-base-v2-sentiment

Fine-tuned albert-base-v2 on the BESSTIE-CW-26 dataset for binary sentiment classification.

Training

• Base model: albert-base-v2
• Task: sentiment (binary)
• Epochs: 2
• Batch size: 4
• Learning rate: 2e-5
• Weight decay: 0.01
• Max sequence length: 64
• Seed: 42 (best of {42, 65, 131})
• Optimizer: AdamW (Trainer default)

Test results

• macro-F1: 0.8662

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("vyshnav112233/albert-base-v2-sentiment")
tokenizer = AutoTokenizer.from_pretrained("vyshnav112233/albert-base-v2-sentiment")
inputs = tokenizer("your sentence here", return_tensors="pt", truncation=True, max_length=64)
logits = model(**inputs).logits