license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pszemraj/govreport-summarization-8192
model-index:
- name: led-base-16384-finetuned-govreport
results: []
language:
- en
pipeline_tag: summarization
led-base-16384-finetuned-govreport
This model is a fine-tuned version of allenai/led-base-16384 on the pszemraj/govreport-summarization-8192 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2887
The amount of processing time and memory required to assess the ROUGE metrics on the validation and test sets were not supported by Kaggle at this moment in time.
Model description
As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, Allenai's Longformer Encoder-Decoder (LED) was initialized from bart-base since both models share the exact same architecture. To be able to process 16K tokens, bart-base's position embedding matrix was simply copied 16 times.
This model is especially interesting for long-range summarization and question answering.
Intended uses & limitations
pszemraj/govreport-summarization-8192 is a pre-processed version of the dataset ccdv/govreport-summarization, which is a dataset for summarization of long documents adapted from this repository and this paper.
The Allenai's LED model was fine-tuned to this dataset, allowing the summarization of documents up to 16384 tokens.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1492 | 0.24 | 250 | 1.4233 |
| 1.0077 | 0.49 | 500 | 1.3813 |
| 1.0069 | 0.73 | 750 | 1.3499 |
| 0.9639 | 0.98 | 1000 | 1.3216 |
| 0.7996 | 1.22 | 1250 | 1.3172 |
| 0.9395 | 1.46 | 1500 | 1.3003 |
| 0.913 | 1.71 | 1750 | 1.2919 |
| 0.8843 | 1.95 | 2000 | 1.2887 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3