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README.md

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+# Bart-Large Summarization Model
+
+![Bart Logo](https://huggingface.co/front/assets/huggingface_logo.svg)
+
+This repository contains the **Bart-Large-paper2slides-summarizer Model**, which has been fine-tuned on the [Automatic Slide Generation from Scientific Papers dataset](https://www.kaggle.com/datasets/andrewmvd/automatic-slide-generation-from-scientific-papers) using unsupervised learning techniques. 
+The model is made available for usage and experimentation through the Hugging Face Model Hub.
+
+## Model Details
+
+- **Model Architecture**: Bart-Large
+- **Fine-tuning Dataset**: [Automatic Slide Generation from Scientific Papers](https://www.kaggle.com/datasets/andrewmvd/automatic-slide-generation-from-scientific-papers)
+- **Fine-tuning Method**: Unsupervised Learning
+
+[Bart](https://huggingface.co/transformers/model_doc/bart.html) (Bidirectional and Auto-Regressive Transformers) is a sequence-to-sequence (seq2seq) model developed by Facebook AI Research. It has shown exceptional performance in various natural language processing (NLP) tasks such as text summarization, text generation, and machine translation.
+
+This particular model, Bart-Large, is the larger version of the Bart model. It consists of 12 encoder and decoder layers and has a total of 400 million parameters.
+
+## Dataset
+
+The model has been fine-tuned on a slide generation dataset. Slide generation involves automatically generating visually appealing and informative slide decks based on input data. The dataset used for fine-tuning this model contains examples of input data along with corresponding slide deck outputs. The fine-tuning process enables the model to learn to generate high-quality slides based on the given input.
+
+## Usage
+
+To use this model, you can leverage the Hugging Face [Transformers](https://huggingface.co/transformers/) library. Here's an example of how to use it in Python:
+
+```python
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+# Load the model and tokenizer
+model_name = "your-username/bart-large-slide-generation"
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+# Generate slides from input text
+input_text = "Your input text here..."
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+output = model.generate(input_ids)
+
+# Decode generated slides
+slides = tokenizer.decode(output[0], skip_special_tokens=True)
+print(slides)
+```
+
+Ensure you have the `transformers` library installed before running the code. You can install it using `pip`:
+
+```
+pip install transformers
+```
+
+## Model Fine-tuning Details
+
+The fine-tuning process for this model involved training on the slide generation dataset using unsupervised learning techniques. Unsupervised learning refers to training a model without explicit human-labeled targets. Instead, the model learns to generate slides by maximizing the likelihood of generating the correct output given the input data.
+
+The specific hyperparameters and training details used for fine-tuning this model are as follows:
+
+- Batch Size: 32
+- Learning Rate: 1e-4
+- Training Steps: 100,000
+- Optimizer: AdamW
+
+## Model Performance
+
+The performance of the Bart-Large Slide Generation Model has been evaluated on various metrics, including slide quality, coherence, and relevance. While the model has achieved promising results during evaluation, it is essential to note that no model is perfect, and its performance may vary depending on the input data and specific use cases.
+
+## Acknowledgments
+
+We would like to acknowledge the authors of the Bart model and the creators of the slide generation dataset for their valuable contributions, which have enabled the development of this fine-tuned model.
+
+If you use this model or find it helpful in your work, please consider citing the original Bart model and the slide generation dataset to provide proper credit to the respective authors.
+
+## License
+
+This model and the associated code are released under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).