| | ---
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| | language:
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| | - en
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| | tags:
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| | - summarization
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| | license: apache-2.0
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| | datasets:
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| | - cnn_dailymail
|
| | ---
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| |
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| | # Try out in the Hosted inference API
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| |
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| | In the right panel, you can try to the model (although it only handles a short sequence length).
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| | Enter the document you want to summarize in the panel on the right.
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| |
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| | # Model Loading
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| | The model (based on a GPT2 base architecture) can be loaded in the following way:
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| | ```
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| | from transformers import GPT2LMHeadModel, GPT2TokenizerFast
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| |
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| | model = GPT2LMHeadModel.from_pretrained("philippelaban/summary_loop10")
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| | tokenizer = GPT2TokenizerFast.from_pretrained("philippelaban/summary_loop10")
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| | ```
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| |
|
| | # Example Use
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| | ```
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| | document = "Bouncing Boulders Point to Quakes on Mars. A preponderance of boulder tracks on the red planet may be evidence of recent seismic activity. If a rock falls on Mars, and no one is there to see it, does it leave a trace? Yes, and it's a beautiful herringbone-like pattern, new research reveals. Scientists have now spotted thousands of tracks on the red planet created by tumbling boulders. Delicate chevron-shaped piles of Martian dust and sand frame the tracks, the team showed, and most fade over the course of a few years. Rockfalls have been spotted elsewhere in the solar system, including on the moon and even a comet. But a big open question is the timing of these processes on other worlds — are they ongoing or did they predominantly occur in the past?"
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| |
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| | tokenized_document = tokenizer([document], max_length=300, truncation=True, return_tensors="pt")["input_ids"].cuda()
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| | input_shape = tokenized_document.shape
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| | outputs = model.generate(tokenized_document, do_sample=False, max_length=500, num_beams=4, num_return_sequences=4, no_repeat_ngram_size=6, return_dict_in_generate=True, output_scores=True)
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| | candidate_sequences = outputs.sequences[:, input_shape[1]:] # Remove the encoded text, keep only the summary
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| | candidate_scores = outputs.sequences_scores.tolist()
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| |
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| | for candidate_tokens, score in zip(candidate_sequences, candidate_scores):
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| | summary = tokenizer.decode(candidate_tokens)
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| | print("[Score: %.3f] %s" % (score, summary[:summary.index("END")]))
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| | ```
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| |
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| | # Example output
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| | ```
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| | [Score: -0.084] Here's what you need to know about rockfalls
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| | [Score: -0.087] Here's what you need to know about these tracks
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| | [Score: -0.091] Here's what we know so far about these tracks
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| | [Score: -0.101] Here's what you need to know about rockfall
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| | ```
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| |
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| | # Github repo
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| |
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| | You can access more information, access to the scoring function, the training script, or an example training log on the Github repo: https://github.com/CannyLab/summary_loop |
