| # 🧠 TextSummarizerForInventoryReport-T5 |
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| 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. |
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| ## ✨ Model Highlights |
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| - 📌 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 |
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| --- |
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| ## 🧠 Intended Uses |
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| - ✅ Inventory report summarization |
| - ✅ Warehouse/logistics management automation |
| - ✅ Business analytics and reporting dashboards |
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| ## 🚫 Limitations |
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| - ❌ 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 |
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| ## 🏋️♂️ Training Details |
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| | 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 | |
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| ## 🚀 Usage |
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| ```python |
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| from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments |
| from datasets import Dataset |
| import torch |
| import torch.nn.functional as F |
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| model_name = "AventIQ-AI/Text_Summarization_For_inventory_Report" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| model.eval() |
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| 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 |
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| # Generate summary |
| summary = summarize(long_text, model, tokenizer) |
| print("Summary:", summary) |
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| ``` |
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| ## Repository Structure |
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| ``` |
| . |
| ├── model/ # Contains the quantized model files |
| ├── tokenizer_config/ # Tokenizer configuration and vocabulary files |
| ├── model.safensors/ # Fine Tuned Model |
| ├── README.md # Model documentation |
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| ``` |
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| 🤝 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. |
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