Update README.md

README.md

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----
-library_name: transformers
-license: apache-2.0
-model_name: MBart-Urdu-Text-Summarization
-pipeline_tag: text-generation
-tags:
-- text-generation
-- mbart
-- nlp
-- transformers
-author: Wali Muhammad Ahmad
-private: false
-gated: false
-inference: true
-mask_token: <mask>
-widget_data:
-  text: Enter your para here
-transformers_info:
-  auto_class: MBartForConditionalGeneration
-  processor: AutoTokenizer
----
-
-# 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. -->
-
-
-
-- **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:** apache-2.0
-- **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. -->
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-#### 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. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
-
-[More Information Needed]
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-#### 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]
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-#### Software
-
-[More Information Needed]
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-## 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]
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-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+library_name: transformers
+license: mit
+model_name: MBart-Urdu-Text-Summarization
+pipeline_tag: summarization
+tags:
+- text-generation
+- mbart
+- nlp
+- transformers
+- text-generation-inference
+author: Wali Muhammad Ahmad
+private: false
+gated: false
+inference: true
+mask_token: <mask>
+widget_data:
+  text: Enter your para here
+transformers_info:
+  auto_class: MBartForConditionalGeneration
+  processor: AutoTokenizer
+language:
+- en
+- ur
+---
+
+# Model Card
+
+MBart-Urdu-Text-Summarization is a fine-tuned MBart model designed for summarizing Urdu text. It leverages the multilingual capabilities of MBart to generate concise and accurate summaries for Urdu paragraphs.
+
+## Model Details
+
+### Model Description
+
+This model is based on the MBart architecture, which is a sequence-to-sequence model pre-trained on multilingual data. It has been fine-tuned specifically for Urdu text summarization tasks. The model is capable of understanding and generating text in both English and Urdu, making it suitable for multilingual applications.
+
+### Model Sources [optional]
+
+- **Repository:** [https://github.com/WaliMuhammadAhmad/UrduTextSummarizationUsingm-BART]
+- **Paper [Multilingual Denoising Pre-training for Neural Machine Translation]:** [https://arxiv.org/abs/2001.08210]
+
+## Uses
+
+### Direct Use
+
+This model can be used directly for Urdu text summarization tasks. It is suitable for applications such as news summarization, document summarization, and content generation.
+
+### Downstream Use [optional]
+
+The model can be fine-tuned for specific downstream tasks such as sentiment analysis, question answering, or machine translation for Urdu and English.
+
+### Out-of-Scope Use
+
+This model is not intended for generating biased, harmful, or misleading content. It should not be used for tasks outside of text summarization without proper fine-tuning and evaluation.
+
+## Bias, Risks, and Limitations
+
+- The model may generate biased or inappropriate content if the input text contains biases.
+- It is trained on a specific dataset and may not generalize well to other domains or languages.
+- The model's performance may degrade for very long input texts.
+
+### Recommendations
+
+Users should carefully evaluate the model's outputs for biases and appropriateness. Fine-tuning on domain-specific data is recommended for better performance in specialized applications.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, MBartForConditionalGeneration
+
+# Load the model and tokenizer
+model_name = "ihatenlp/MBart-Urdu-Text-Summarization"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = MBartForConditionalGeneration.from_pretrained(model_name)
+
+# Example input text
+input_text = "Enter your Urdu paragraph here."
+
+# Tokenize and generate summary
+inputs = tokenizer(input_text, return_tensors="pt")
+summary_ids = model.generate(inputs["input_ids"], max_length=50, num_beams=4, early_stopping=True)
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+print("Summary:", summary)
+```
+
+## Environmental Impact
+
+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).
+
+## Citation [optional]
+
+**BibTeX:**
+
+```bibtex
+@misc{liu2020multilingualdenoisingpretrainingneural,
+      title={Multilingual Denoising Pre-training for Neural Machine Translation}, 
+      author={Yinhan Liu and Jiatao Gu and Naman Goyal and Xian Li and Sergey Edunov and Marjan Ghazvininejad and Mike Lewis and Luke Zettlemoyer},
+      year={2020},
+      eprint={2001.08210},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2001.08210}, 
+}
+```
+
+## Model Card Authors [optional]
+
+- **Wali Muhammad Ahmad**
+- **Muhammad Labeeb Tariq**
+
+## Model Card Contact
+
+- **Email:** [[email protected]]
+- **Hugging Face Profile:** [Wali Muhammad Ahmad](https://huggingface.co/ihatenlp)