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README.md

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+# 🧠 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
+
+```python
+
+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
+python
+Copy
+Edit
+.
+├── model/               # Contains fine-tuned model files
+├── tokenizer/           # Tokenizer config and vocab
+├── config.json          # Model configuration
+├── pytorch_model.bin    # Fine-tuned model weights
+├── README.md            # Model card
+
+
+
+🤝 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.
+
+
+
+
+