bigbird pegasus on the booksum dataset
this is the "latest" version of the model that has been trained the longest, currently at 70k steps
- GOAL: A summarization model that 1) summarizes the source content accurately 2) more important IMO produces summaries that are easy to read and understand (* cough * unlike arXiv * cough *)
- This model attempts to help with that by using the booksum dataset to provide explanatory summarization
- Explanatory Summary - A summary that both consolidates information and also explains why said consolidated information is important.
- This model was trained for seven epochs total (approx 70,000 steps) and is closer to finished.
- Will continue to improve (slowly, now that it has been trained for a long time) based on any result findings/feedback.
- starting checkpoint was
google/bigbird-pegasus-large-bigpatent
example usage
An extended example, including a demo of batch summarization, is here.
- create the summarizer object:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline
_model = AutoModelForSeq2SeqLM.from_pretrained(
"pszemraj/bigbird-pegasus-large-K-booksum",
low_cpu_mem_usage=True,
)
_tokenizer = AutoTokenizer.from_pretrained(
"pszemraj/bigbird-pegasus-large-K-booksum",
)
summarizer = pipeline(
"summarization",
model=_model,
tokenizer=_tokenizer
)
- define text to be summarized, and pass it through the pipeline. Boom done.
wall_of_text = "your text to be summarized goes here."
result = summarizer(
wall_of_text,
min_length=16,
max_length=256,
no_repeat_ngram_size=3,
clean_up_tokenization_spaces=True,
)
print(result[0]['summary_text'])
Alternate Checkpoint
- if experiencing runtime/memory issues, try this earlier checkpoint at 40,000 steps which is almost as good at the explanatory summarization task but runs faster.
Results
- note that while the dataset has three subsets (chapter, book, paragraph) - see the paper. the scores below are run in aggregate. The paper has some benchmark scores listed, which this model competes with.
- note that eval generations are run & computed at a length of 128 tokens.
'eval_gen_len': 126.9791,
'eval_loss': 4.00944709777832,
'eval_rouge1': 27.6028,
'eval_rouge2': 4.6556,
'eval_rougeL': 14.5259,
'eval_rougeLsum': 25.6632,
'eval_runtime': 29847.4812,
'eval_samples_per_second': 0.05,
'eval_steps_per_second': 0.05}
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