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56 lines
1.9 KiB
Plaintext
56 lines
1.9 KiB
Plaintext
inputs:
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chat_history:
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type: list
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default:
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- inputs:
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question: what is BERT?
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outputs:
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answer: BERT (Bidirectional Encoder Representations from Transformers) is a
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language representation model that pre-trains deep bidirectional
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representations from unlabeled text by jointly conditioning on both
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left and right context in all layers. Unlike other language
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representation models, BERT can be fine-tuned with just one additional
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output layer to create state-of-the-art models for a wide range of
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tasks such as question answering and language inference, without
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substantial task-specific architecture modifications. BERT is
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effective for both fine-tuning and feature-based approaches. It
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obtains new state-of-the-art results on eleven natural language
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processing tasks, including pushing the GLUE score to 80.5% (7.7%
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point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute
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improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point
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absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point
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absolute improvement).
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pdf_url:
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type: string
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default: https://arxiv.org/pdf/1810.04805.pdf
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question:
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type: string
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is_chat_input: true
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default: what NLP tasks does it perform well?
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outputs:
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answer:
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type: string
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is_chat_output: true
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reference: ${chat_with_pdf_tool.output.answer}
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context:
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type: string
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reference: ${chat_with_pdf_tool.output.context}
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nodes:
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- name: setup_env
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type: python
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source:
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type: code
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path: setup_env.py
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inputs:
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conn: my_custom_connection
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- name: chat_with_pdf_tool
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type: python
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source:
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type: code
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path: chat_with_pdf_tool.py
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inputs:
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history: ${inputs.chat_history}
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pdf_url: ${inputs.pdf_url}
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question: ${inputs.question}
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ready: ${setup_env.output}
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