# Model Card: t5-summary-finetuned-kw-fp16
# Model Overview
- **Model Name:** t5-summary-finetuned-kw-fp16
- **Base Model:** T5-base (t5-base from Hugging Face)
- **Date:** March 19, 2025
- **Version:** 1.0
- **Task:** Keyword-Based Text Summarization
- **Description:** A fine-tuned T5-base model quantized to FP16 for generating concise summaries from short text inputs, guided by a user-specified keyword. Trained on a custom dataset of 200 examples, it produces summaries focusing on the keyword while maintaining a professional tone.
# Model Details
- **Architecture:** Encoder-Decoder Transformer (T5-base)
- **Parameters:** ~223M (original T5-base), quantized to FP16
- **Precision:** FP16 (16-bit floating-point)
- **Input Format:** Text paragraph + "Keyword: [keyword]" (e.g., "The storm caused heavy rain and wind damage. Keyword: rain")
- **Output Format:** Concise summary (1-2 sentences) focusing on the keyword (e.g., "The storm brought heavy rain overnight.")
- **Training Hardware:** NVIDIA GPU with 12 GB VRAM (e.g., RTX 3060)
- **Inference Hardware:** Compatible with GPUs supporting FP16 (minimum ~1.5 GB VRAM)
# Training Data
**Dataset Name:** Custom Keyword-Based Summarization Dataset
- **Size:** 200 examples
- **Split:** 180 training, 20 validation
- **Format:** CSV 
- **input:** Paragraph (2-4 sentences) + "Keyword: [keyword]"
- **keyword:** Single word or short phrase guiding the summary
- **output:** Target summary (1-2 sentences)
- **Content:** Diverse topics including tech, weather, sports, health, and culture (e.g., "A new laptop was released with a fast processor... Keyword: processor" → "The new laptop has a fast processor.")
- **Language:** English
# Training Procedure
- **Framework:** PyTorch via Hugging Face Transformers
# Hyperparameters:
**Epochs:** 2 (stopped early; originally set for 3)
- **Learning Rate:** 3e-4
- **Batch Size:** 4 (effective 8 with gradient accumulation)
- **Warmup Steps:** 5
- **Weight Decay:** 0.01
- **Precision:** FP16 (mixed precision training)
- **Training Time:** ~1.5 minutes on a 12 GB GPU
# Loss:
- **Training:** 1.0099 (epoch 1) → 0.3479 (epoch 2)
- **Validation:** 1.0176 (epoch 1, best) → 1.0491 (epoch 2)
# Performance
- **Metrics:** Validation loss (best: 1.0176)
- **Qualitative Evaluation:** Generates concise, keyword-focused summaries with good coherence (e.g., "The concert featured a famous singer" for keyword "singer").
# Intended Use
- Purpose: Summarize short texts (e.g., news snippets, reports) based on a user-specified keyword.
- Use Case: Quick summarization for journalists, researchers, or content creators needing keyword-driven insights.
- Out of Scope: Not designed for long documents (>128 tokens) or abstractive summarization without keywords.
# Usage Instructions
```
Requirements
Python 3.8+
Libraries: transformers, torch, pandas
GPU with FP16 support (e.g., NVIDIA with ~1.5 GB VRAM free)
```
# Example Code
```python

from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("./t5_summary_finetuned_final_fp16").to("cuda")
tokenizer = T5Tokenizer.from_pretrained("./t5_summary_finetuned_final_fp16")

# Generate summary
text = "A new laptop was released with a fast processor and sleek design. It’s popular among gamers."
keyword = "processor"
input_text = f"{text} Keyword: {keyword}"
inputs = tokenizer(input_text, max_length=128, truncation=True, padding="max_length", return_tensors="pt").to("cuda")
outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"].to(torch.float16), max_length=128, num_beams=4, early_stopping=True, no_repeat_ngram_size=2)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(summary)  # Expected: "The new laptop has a fast processor."
```