---
tags:
- generated_from_trainer
datasets:
- allenai/mslr2022
model-index:
- name: baseline
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Overview

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [allenai/mslr2022](https://huggingface.co/datasets/allenai/mslr2022) ms2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.7527
- eval_rouge1_fmeasure_mean: 27.9314
- eval_rouge2_fmeasure_mean: 9.4000
- eval_rougeL_fmeasure_mean: 20.9302
- eval_rougeLsum_fmeasure_mean: 23.6179
- eval_bertscore_hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.21.0.dev0)-rescaled_fast-tokenizer
- eval_bertscore_f1_mean: 23.5092
- eval_seed: 42
- eval_model_name_or_path: output/ms2/led-base/baseline
- eval_doc_sep_token: </s>
- eval_runtime: 820.6405
- eval_samples_per_second: 2.463
- eval_steps_per_second: 0.617
- step: 0

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 2.3.3.dev0
- Tokenizers 0.12.1