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---
tags:
- summarization
- summary
- booksum
- long-document
- long-form
license:
- apache-2.0
- bsd-3-clause
datasets:
- kmfoda/booksum
metrics:
- rouge
inference: false
model-index:
- name: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 24.5482
verified: true
- name: ROUGE-2
type: rouge
value: 4.811
verified: true
- name: ROUGE-L
type: rouge
value: 17.2505
verified: true
- name: ROUGE-LSUM
type: rouge
value: 20.906
verified: true
- name: loss
type: loss
value: 3.3002164363861084
verified: true
- name: gen_len
type: gen_len
value: 52.0
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 35.3305
verified: true
- name: ROUGE-2
type: rouge
value: 5.8001
verified: true
- name: ROUGE-L
type: rouge
value: 16.0557
verified: true
- name: ROUGE-LSUM
type: rouge
value: 32.3409
verified: true
- name: loss
type: loss
value: 3.0063540935516357
verified: true
- name: gen_len
type: gen_len
value: 268.4032
verified: true
---
# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15
This model is a fine-tuned version of [pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13](https://huggingface.co./pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13.5) on the `kmfoda/booksum` dataset.
## 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: 0.0004
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.4
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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