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--- |
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tags: |
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- summarization |
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- summary |
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- booksum |
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- long-document |
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- long-form |
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- lsg |
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datasets: |
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- kmfoda/booksum |
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metrics: |
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- rouge |
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model-index: |
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- name: ccdv/lsg-bart-base-4096-booksum |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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**This model relies on a custom modeling file, you need to add trust_remote_code=True**\ |
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**See [\#13467](https://github.com/huggingface/transformers/pull/13467)** |
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LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \ |
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Github/conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg). |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-booksum", trust_remote_code=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-booksum", trust_remote_code=True) |
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text = "Replace by what you want." |
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pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0) |
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generated_text = pipe( |
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text, |
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truncation=True, |
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max_length=64, |
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no_repeat_ngram_size=7, |
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num_beams=2, |
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early_stopping=True |
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) |
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``` |
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# ccdv/lsg-bart-base-4096-booksum |
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This model is a fine-tuned version of [ccdv/lsg-bart-base-4096](https://huggingface.co/ccdv/lsg-bart-base-4096) on the kmfoda/booksum kmfoda--booksum dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 3.2654 |
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- eval_rouge1: 33.9468 |
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- eval_rouge2: 6.7034 |
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- eval_rougeL: 16.7879 |
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- eval_rougeLsum: 31.7677 |
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- eval_gen_len: 427.6918 |
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- eval_runtime: 2910.3841 |
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- eval_samples_per_second: 0.492 |
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- eval_steps_per_second: 0.062 |
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- eval_samples: 1431 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30.0 |
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### Generate hyperparameters |
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The following hyperparameters were used during generation: |
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- dataset_name: kmfoda/booksum |
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- dataset_config_name: kmfoda--booksum |
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- eval_batch_size: 8 |
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- eval_samples: 1431 |
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- early_stopping: True |
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- ignore_pad_token_for_loss: True |
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- length_penalty: 2.0 |
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- max_length: 512 |
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- min_length: 128 |
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- num_beams: 5 |
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- no_repeat_ngram_size: None |
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- seed: 123 |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.3.2 |
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- Tokenizers 0.11.6 |
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