---
title: led-base-16384-ms2
canonical_url: "https://www.modelscope.cn/models/LLM-Research/led-base-16384-ms2"
md_url: "https://www.modelscope.cn/models/LLM-Research/led-base-16384-ms2.md"
repository: LLM-Research/led-base-16384-ms2
last_updated: 2024-09-08
pipeline_tag: text2text-generation
tasks:
  - text2text-generation
model_type:
  - led
architectures:
  - LEDForConditionalGeneration
base_model:
  - allenai/led-base-16384
base_model_relation: finetune
parameters: 161.9M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 2075
stars: 0
tags:
  - generated_from_trainer
---

# led-base-16384-ms2

> led-base-16384-ms2 - LLM-Research 在 ModelScope 开源的模型。This model is a fine-tuned version of allenai/led-base-16384 on the MS^2 dataset. The model received as input the background section and the titles and abstracts of up to 25 included studies for each…

LLM-Research/led-base-16384-ms2 是 ModelScope 魔搭社区上的 161.9M 参数text2text-generation模型，基于 allenai/led-base-16384 构建。

- **Repository**: LLM-Research/led-base-16384-ms2
- **Tasks**: text2text-generation
- **Parameters**: 161.9M
- **Base model**: allenai/led-base-16384
- **Tags**: generated_from_trainer
- **Downloads**: 2075
- **Stars**: 0
- **Last updated**: 2024-09-08

Source: https://www.modelscope.cn/models/LLM-Research/led-base-16384-ms2

---

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# Overview

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [MS^2](https://github.com/allenai/mslr-shared-task#ms2-dataset) dataset. The model received as input the background section and the titles and abstracts of up to 25 included studies for each example, concatenated by the `"</s>"` token. Global attention is applied to the special start token `"<s>"` and each of the document separator tokens `"</s>"`. The model slightly outperforms the reported results in the original paper: [MS2: Multi-Document Summarization of Medical Studies](https://arxiv.org/abs/2104.06486). See the [MS2 leaderboard](https://leaderboard.allenai.org/mslr-ms2/submissions/public) for results on the blind test set. 

It achieves the following results on the evaluation set:
- Loss: 3.7602
- Rouge1 Fmeasure Mean: 28.5338
- Rouge2 Fmeasure Mean: 9.5060
- RougeL Fmeasure Mean: 20.9321
- RougeLsum Fmeasure Mean: 24.0998
- Bertscore Hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.21.0.dev0)-rescaled_fast-tokenizer
- Bertscore F1 Mean: 22.7619
- Seed: 42
- Model Name Or Path: allenai/led-base-16384
- Doc Sep Token: `"</s>"`

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.10.0
- Datasets 2.4.0
- Tokenizers 0.12.1