Update README.md

README.md

@@ -19,7 +19,7 @@ pipeline_tag: summarization
 
 ![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 using an algorithm from the paper entitled 'unsupervised machine translation'.
+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 using an algorithm from the paper entitled '[Unsupervised Machine Translation Using Monolingual Corpora Only](https://arxiv.org/abs/1711.00043)'.
 Its primary focus is to summarize **scientific texts** with precision and accuracy, the model is parallelly trained with another model from the same contributor, 
 
 ## Model Details
@@ -40,18 +40,23 @@ To use this model, you can leverage the Hugging Face [Transformers](https://hugg
 from transformers import BartTokenizer, BartForConditionalGeneration
 
 # Load the model and tokenizer
-model_name = "your-username/bart-large-slide-generation"
+model_name = "com3dian/Bart-large-paper2slides-summarizer"
 tokenizer = BartTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 
-# Generate slides from input text
+# Generate summary 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)
+# Decode generated summaries
+summary = tokenizer.decode(output[0], skip_special_tokens=True)
+print(summary)
+
+# Or using the pipeline API
+summarizer = pipeline("summarization", model=model_name)
+summary = summarizer(text, max_length=50, min_length=30, do_sample=False)
+print(summary)
 ```
 
 Ensure you have the `transformers` library installed before running the code. You can install it using `pip`: