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This model is a fine-tuned version of the Longformer Encoder-Decoder (LED)- "allenai/led-base-16384", specifically tailored for [describe the task, e.g., "summarizing scientific articles"]. LED, originally designed for long document tasks, leverages a sparse attention mechanism to handle much longer contexts than standard transformer models. Our version extends its capabilities to efficiently summarize texts with high fidelity and relevance.

This Model can handle a total input token of upto "16000" tokens which is larger than most of the models present out there.

Base code is specified below, try that out, API example wont work as tokenizer of allenai/led-base-16384 is used!


This model is intended for use in scenarios where understanding and condensing long texts is necessary. It is particularly useful for:

Academic researchers needing summaries of lengthy papers. Professionals who require digests of extensive reports. Content creators looking for concise versions of long articles.

Please note: This model will work for any summarization process to generate abstractive summary, just keep in mind to get the best results for a particular domain, you need to train the model on your specific dataset if for a specific domain.


The only limitation you might face is, to get the best results, you will have to fine-tune it. LOL!!

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
#Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("yashrane2904/LED_Finetuned").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384") # Since it is a fine-tuned version of led-base-16348, we use the same tokenizer as that model used
LONG_ARTICLE = "Your long text goes here..."
#Tokenize the input article
input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda")
global_attention_mask = torch.zeros_like(input_ids)
global_attention_mask[:, 0] = 1
#Generate summaries
sequences_tensor = model.generate(input_ids, global_attention_mask=global_attention_mask, num_beams=10, num_beam_groups=1,repetition_penalty=6.0,max_length=600,min_length=350,temperature=1.5)
sequences = sequences_tensor.tolist() # Convert Tensor to list of token IDs
summary = tokenizer.batch_decode(sequences, skip_special_tokens=True) # Decode token IDs into text
#Print the generated summary

Feel free to play around with the hyperparameters in the generate, or some other parameters to include for experimentation purpose.

Model Card Authors & Citation

@misc {yash_rane_2024,
author = { {Yash Rane} },
title = { LED_Finetuned (Revision f480282) },
year = 2024,
url = { https://huggingface.co/yashrane2904/LED_Finetuned },
doi = { 10.57967/hf/2074 },
publisher = { Hugging Face }

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