🧠 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.