Update README.md

README.md

@@ -3,26 +3,28 @@ datasets:
 - bakhitovd/data_science_arxiv
 metrics:
 - rouge
+license: cc0-1.0
+pipeline_tag: summarization
 ---
 # Fine-tuned Longformer for Summarization of Machine Learning Articles
 
 ## Model Details
 - GitHub: https://github.com/Bakhitovd/MS_in_Data_Science_Capstone
-- Model name: bakhitovd/led-base-16384-data-science
+- Model name: bakhitovd/led-base-7168-ml
 - Model type: Longformer (alenai/led-base-16384)
 - Model description: This Longformer model has been fine-tuned on a focused subset of the arXiv part of the scientific papers dataset, specifically targeting articles about Machine Learning. It aims to generate accurate and consistent summaries of machine learning research papers.
 ## Intended Use
 This model is intended to be used for text summarization tasks, specifically for summarizing machine learning research papers.
 ## How to Use
-~~~
+```
 import torch
 from transformers import LEDTokenizer, LEDForConditionalGeneration
-tokenizer = LEDTokenizer.from_pretrained("bakhitovd/led-base-16384-data-science")
-model = LEDForConditionalGeneration.from_pretrained("bakhitovd/led-base-16384-data-science")
-~~~
+tokenizer = LEDTokenizer.from_pretrained("bakhitovd/led-base-7168-ml")
+model = LEDForConditionalGeneration.from_pretrained("bakhitovd/led-base-7168-ml")
+```
 
 ## Use the model for summarization
-~~~
+```
 article = "... long document ..."
 inputs_dict = tokenizer.encode(article, padding="max_length", max_length=16384, return_tensors="pt", truncation=True)
 input_ids = inputs_dict.input_ids.to("cuda")
@@ -32,7 +34,7 @@ global_attention_mask[:, 0] = 1
 predicted_abstract_ids = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, max_length=512)
 summary = tokenizer.decode(predicted_abstract_ids, skip_special_tokens=True)
 print(summary)
-~~~
+```
 ## Training Data
 Dataset name: bakhitovd/data_science_arxiv
 This dataset is a subset of the 'Scientific papers' dataset, which contains articles semantically, structurally, and meaningfully closest to articles describing machine learning. This subset was obtained using K-means clustering on the embeddings generated by SciBERT.