Dragneel
/

nepali-article-title-generator

@@ -1,199 +1,80 @@
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- sanjeev-bhandari01/nepali-summarization-dataset
+base_model:
+- facebook/mbart-large-cc25
 ---
+# Nepali Article Title Generator
+This is a fine-tuned MBart model for generating titles (summaries) for Nepali articles. The model was fine-tuned on a Nepali summarization dataset and is designed to generate concise and relevant titles for given Nepali text.
 ## Model Details
+- **Model Type**: MBart (Multilingual BART)
+- **Fine-Tuned For**: Nepali Article Title Generation
+- **Languages**: Nepali (`ne_NP`)
+- **Model Size**: Large
+- **Training Dataset**: Nepali Summarization Dataset (first 50,000 samples)
+- **Fine-Tuning Framework**: Hugging Face Transformers
+- **Fine-Tuning Epochs**: 3
+- **Max Input Length**: 512 tokens
+- **Max Target Length**: 128 tokens
+## How to Use
+You can use this model to generate titles for Nepali articles. Below is an example of how to load and use the model.
+### Installation
+First, install the required libraries:
+```bash
+pip install torch transformers
+```
+```bash
+import torch
+from transformers import MBartTokenizer, MBartForConditionalGeneration
+# Load the fine-tuned model
+MODEL_PATH = "Dragneel/nepali-article-title-generator"
+model = MBartForConditionalGeneration.from_pretrained(MODEL_PATH)
+tokenizer = MBartTokenizer.from_pretrained(MODEL_PATH)
+# Set the source and target language
+tokenizer.src_lang = "ne_NP"
+tokenizer.tgt_lang = "ne_NP"
+# Define the input text
+input_text = """
+नेपालको पर्यटन उद्योगमा कोरोनाको प्रभावले गर्दा ठूलो मन्दी आएको छ।
+विश्वभर यात्रा प्रतिबन्ध लागू भएपछि नेपाल आउने पर्यटकको संख्या न्यून भएको छ।
+यसले गर्दा होटल, यातायात र अन्य पर्यटन सम्बन्धी व्यवसायमा ठूलो असर परेको छ।
+सरकारले पर्यटन उद्योगलाई बचाउन विभिन्न उपायहरू ल्याएको छ, तर अहिलेसम्म कुनै ठूलो सुधार देखिएको छैन।
+"""
+# Tokenize the input
+inputs = tokenizer(
+    input_text,
+    return_tensors="pt",
+    max_length=512,
+    truncation=True,
+    padding="max_length"
+)
+# Generate summary
+model.eval()  # Set the model to evaluation mode
+with torch.no_grad():
+    output_ids = model.generate(
+        inputs["input_ids"],
+        max_length=128,  # Set the maximum length of the generated text
+        num_beams=4,  # Beam search for better quality
+        early_stopping=True
+    )
+# Decode the generated text
+summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print("Generated Title:", summary)
+```