metadata
license: apache-2.0
widget:
- text: >-
पश्चिम नेपालमा भारी वर्षाले धेरै पहिरो निम्त्याएको छ, जसले पूर्वाधार र
घरहरूमा ठूलो क्षति पुर्याएको छ। कम्तिमा पाँच जना हराइरहेका छन् र उद्धार
कार्य जारी छ। अधिकारीहरूले प्रभावित क्षेत्रका बासिन्दाहरूलाई सतर्क रहन र
सुरक्षात्मक दिशानिर्देशहरू पालना गर्न चेतावनी दिएका छन्।
language:
- ne_NP tags:
- summarization
- ne_NP license: apache-2.0 datasets:
- sanjeev-bhandari01/nepali-summarization-dataset
metrics:
- rouge
model_name: "MBart Nepali Summarization Model"
model_description: >
This model is a fine-tuned version of the facebook/mbart-large-cc25 on the Nepali Summarization Dataset. It is designed to generate concise summaries of Nepali articles.
intended_use: > The model is intended for use in summarizing Nepali news articles or other textual content written in Nepali. It can be utilized in applications such as news aggregation, content summarization, and aiding in quick information retrieval.
how_to_use: |
To use this model, you can load it with the transformers library as follows:
from transformers import MBartTokenizer, MBartForConditionalGeneration
model_name = 'path_to_your_finetuned_model'
tokenizer = MBartTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
# Example text
article = "तपाईंको नेपाली समाचार लेख यहाँ राख्नुहोस्।"
# Tokenize the input
inputs = tokenizer(article, return_tensors="pt", max_length=1024, truncation=True)
# Generate summary
summary_ids = model.generate(inputs['input_ids'], max_length=128, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)