🧠 TextSummarizerForInventoryReport-T5

A T5-based text summarization model fine-tuned on inventory report data. This model generates concise summaries of detailed inventory-related texts, making it useful for warehouse management, stock reporting, and supply chain documentation.

✨ Model Highlights

📌 Based on t5-small from Hugging Face 🤗
🔍 Fine-tuned on structured inventory report data (report_text → summary_text)
📋 Generates meaningful and human-readable summaries
⚡ Supports maximum input length of 512 tokens and output length of 128 tokens
🧠 Built using Hugging Face Transformers and PyTorch

🧠 Intended Uses

✅ Inventory report summarization
✅ Warehouse/logistics management automation
✅ Business analytics and reporting dashboards

🚫 Limitations

❌ Not optimized for very long reports (>512 tokens)
🌍 Trained primarily on English-language technical/business reports
🧾 Performance may degrade on unstructured or noisy input text
🤔 Not designed for creative or narrative summarization

🏋️‍♂️ Training Details

Attribute	Value
Base Model	t5-small
Dataset	Custom inventory reports
Max Input Tokens	512
Max Output Tokens	128
Epochs	3
Batch Size	2
Optimizer	AdamW
Loss Function	CrossEntropyLosS(with -100 padding mask)
Framework	PyTorch + Hugging Face Transformers
Hardware	CUDA-enabled GPU

🚀 Usage


from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments
from datasets import Dataset
import torch
import torch.nn.functional as F

model_name = "AventIQ-AI/Text_Summarization_For_inventory_Report"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

def preprocess(example):
    input_text = "summarize: " + example["full_text"]
    input_enc = tokenizer(input_text, truncation=True, padding="max_length", max_length=512)
    target_enc = tokenizer(example["summary"], truncation=True, padding="max_length", max_length=64)
    input_enc["labels"] = target_enc["input_ids"]
    return input_enc

# Generate summary
summary = summarize(long_text, model, tokenizer)
print("Summary:", summary)

Repository Structure

.
 ├── model/               # Contains the quantized model files
 ├── tokenizer_config/    # Tokenizer configuration and vocabulary files
 ├── model.safensors/     # Fine Tuned Model
 ├── README.md            # Model documentation

🤝 Contributing Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions, improvements, or want to adapt the model to new domains.