yhamidullah
/

custom-classifier-demo

@@ -1,199 +1,97 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: apache-2.0
+tags:
+  - summarization
+  - custom-model
+  - pegasus
+  - seq2seq
+  - huggingface
+  - transformers
 library_name: transformers
+inference: false
+model-index:
+  - name: Custom Pegasus Summarizer
+    results: []
 ---
+# 🦅 Custom Pegasus Summarizer
+This model is a **custom-wrapped version** of \[`google/pegasus-xsum`\](https://huggingface.co/google/pegasus-xsum) built for **summarization tasks**. It\'s implemented using Hugging Face\'s \`transformers\` library and wrapped with a custom model class for educational and experimental flexibility.
+✅ It supports:
+- Easy fine-tuning and extension \(e.g., adapters, prompt tuning\)
+- Drop-in replacement for the original model
+- Hugging Face Hub compatibility
+- Works with \`AutoTokenizer\` and \`CustomSeq2SeqModel\`
+---
+## 🧠 Model Architecture
+- **Base**: google/pegasus-xsum
+- **Wrapper**: CustomSeq2SeqModel \(inherits from PreTrainedModel\)
+- **Tokenizer**: AutoTokenizer from the same repo
+- **Configuration**: CustomSeq2SeqConfig \(inherits from PretrainedConfig\)
+---
+## 🧪 Training Details
+- **Dataset**: xsum \(500-sample subset\)
+- **Task**: Abstractive Summarization
+- **Epochs**: 1
+- **Batch Size**: 4
+- **Learning Rate**: 2e-5
+- **Training Framework**: Hugging Face Trainer
+---
+## 💡 Usage Example
+\`\`\`python
+from transformers import AutoTokenizer
+from model import CustomSeq2SeqModel  # Your custom wrapper
+tokenizer = AutoTokenizer.from_pretrained("your-username/custom-pegasus-summarizer")
+model = CustomSeq2SeqModel.from_pretrained("your-username/custom-pegasus-summarizer")
+text = "summarize: The Apollo program was a major milestone in space exploration..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True)
+summary_ids = model.generate(**inputs, max_length=60)
+print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
+\`\`\`
+---
+## 🎛 Live Demos
+You can try this model interactively on Hugging Face Spaces:
+- Gradio App: https://huggingface.co/spaces/your-username/custom-pegasus-gradio
+- Streamlit App: https://huggingface.co/spaces/your-username/custom-pegasus-streamlit
+---
+## 📦 Files Included
+- \`config.json\` – Model configuration \(used by \`from_pretrained\`\)
+- \`pytorch_model.bin\` – Fine-tuned model weights
+- \`tokenizer_config.json\` – Tokenizer settings
+- \`vocab.json\` / \`merges.txt\` – Tokenizer vocab \(depends on tokenizer type\)
+- \`special_tokens_map.json\` – Special tokens for summarization
+- \`README.md\` – This model card
+- \`model.py\` – \(if included\) Your \`CustomSeq2SeqModel\` class
+---
+## 📜 License
+Apache 2.0 — same license as the original \`pegasus-xsum\`.
+---
+## 🙏 Acknowledgments
+- Hugging Face for \`transformers\`, \`datasets\`, and \`hub\`
+- Authors of PEGASUS
+- Educational/Research communities building custom NLP models