AnirbanSaha/llama32-3b-tlink-full-finetune

LLaMA 3.2 3B Fine-Tuned for TLINK Classification

🧠 Overview

This model is a fully fine-tuned version of meta-llama/Llama-3.2-3B-Instruct for temporal relation classification (TLINK task).

It predicts the temporal relationship between events in text.

Labels:

• —BEFORE
• —AFTER
• —OTHER
• —NONE ---

📊 Task

Temporal Relation Classification Given a sentence, the model predicts the temporal relationship between events.

📚 Dataset

• —Name: fahmidiqbal/tlink-classification
• —Format: JSONL (text + label)
• —Labels: BEFORE, AFTER, OTHER, NONE
• —Distribution: Balanced (50 samples per class in test set)

⚙️ Training Details

• —Model: meta-llama/Llama-3.2-3B-Instruct
• —Fine-tuning: Full fine-tuning (all parameters updated)
• —Epochs: 3
• —Learning Rate: 2e-5
• —Batch Size: 1 (with gradient accumulation)
• —Optimizer: AdamW
• —Precision: bfloat16
• —Tracking: Weights & Biases (wandb)

📈 Evaluation

Validation Performance

Test Performance

Per-Class Performance (Test Set)

🔬 Analysis

• —The model achieves strong overall performance with a Macro-F1 of ~0.80 on the test set.
• —OTHER and NONE classes show the highest performance.
• —The AFTER class has lower precision but strong recall, indicating some over-prediction.
• —The drop from validation (0.83 F1) to test (0.79 F1) suggests a mild generalization gap, but overall stable performance.
• —Errors are mainly observed in:
• —BEFORE vs AFTER confusion
• —NONE vs OTHER boundary ambiguity

🚀 Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "AnirbanSaha/llama32-3b-tlink-full-finetune"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "The patient developed fever before taking the medication."

inputs = tokenizer(text, return_tensors="pt", truncation=True)

outputs = model(**inputs)
pred = outputs.logits.argmax(dim=-1).item()

labels = ["BEFORE", "AFTER", "OTHER", "NONE"]
print(labels[pred])

⚠️ Important Notice (LLaMA License)

This model is based on:

👉 meta-llama/Llama-3.2-3B-Instruct

You must request access and accept the license:

https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct

Otherwise, loading this model will fail.

📊 Training Logs

Weights & Biases run: 👉 https://wandb.ai/anirbansaha002-univeristy-of-north-texas/llama32-3b-full-finetune/runs/mrdwp04g

🧩 Limitations

• —Performance depends on dataset distribution
• —May not generalize well to unseen domains
• —Sensitive to long input truncation
• —Some confusion between temporally similar classes (BEFORE vs AFTER)

🔬 Research Context

This model was developed as part of research on:

• —Temporal reasoning in NLP
• —Relation classification using large language models

📬 Contact

Author: Anirban Saha Anik Affiliation: University of North Texas

⭐ Citation

If you use this model in your research, please cite appropriately.