File size: 3,206 Bytes
ebd8974 3ab0688 ebd8974 705abc8 ebd8974 705abc8 ebd8974 9d186dc ebd8974 9d186dc ebd8974 9d186dc ebd8974 9d186dc ebd8974 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
# 🧠 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
```
.
├── 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.
|