pszemraj commited on
Commit
e950125
1 Parent(s): 0cef1e2

updates-v2

Browse files
Files changed (2) hide show
  1. app.py +1 -2
  2. summarize.py +3 -3
app.py CHANGED
@@ -26,7 +26,6 @@ logging.basicConfig(
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  MODEL_OPTIONS = [
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  "pszemraj/led-large-book-summary",
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- "pszemraj/led-large-book-summary-continued",
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  "pszemraj/led-base-book-summary",
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  ]
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@@ -341,7 +340,7 @@ if __name__ == "__main__":
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  "- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a Colab notebook for a tutorial."
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  )
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  gr.Markdown(
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- "- **Update May 1, 2023:** Enabled faster inference times via `use_cache=True`, the number of words the model will processed has been increased! New [test model](https://huggingface.co/pszemraj/led-large-book-summary-continued) as an extension of `led-large-book-summary`."
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  )
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  gr.Markdown("---")
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  MODEL_OPTIONS = [
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  "pszemraj/led-large-book-summary",
 
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  "pszemraj/led-base-book-summary",
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  ]
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  "- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a Colab notebook for a tutorial."
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  )
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  gr.Markdown(
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+ "- **Update May 1, 2023:** Enabled faster inference times via `use_cache=True`, the number of words the model will processed has been increased! Not on this demo, but there is a [test model](https://huggingface.co/pszemraj/led-large-book-summary-continued) available: an extension of `led-large-book-summary`."
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  )
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  gr.Markdown("---")
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summarize.py CHANGED
@@ -127,7 +127,7 @@ def summarize_via_tokenbatches(
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  in_id_arr, att_arr = encoded_input.input_ids, encoded_input.attention_mask
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  gen_summaries = []
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- pbar = tqdm(total=len(in_id_arr))
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  for _id, _mask in zip(in_id_arr, att_arr):
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  result, score = summarize_and_score(
@@ -144,9 +144,9 @@ def summarize_via_tokenbatches(
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  "summary_score": score,
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  }
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  gen_summaries.append(_sum)
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- print(f"\t{result[0]}\nScore:\t{score}")
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  pbar.update()
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  pbar.close()
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-
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  return gen_summaries
 
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  in_id_arr, att_arr = encoded_input.input_ids, encoded_input.attention_mask
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  gen_summaries = []
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+ pbar = tqdm(total=len(in_id_arr), desc="Summarizing")
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  for _id, _mask in zip(in_id_arr, att_arr):
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  result, score = summarize_and_score(
 
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  "summary_score": score,
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  }
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  gen_summaries.append(_sum)
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+ logger.info(f"SCore {score} for summary:\n\t{result}")
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  pbar.update()
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  pbar.close()
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+ logger.debug(f"Generated summaries:\n{pp.pformat(gen_summaries)}")
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  return gen_summaries