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README.md
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# ehdwns1516/bert-base-uncased_SWAG
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* This model has been trained as a [SWAG dataset](https://huggingface.co/ehdwns1516/bert-base-uncased_SWAG).
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Sentence Inference Multiple Choice DEMO: [Ainize DEMO](https://main-sentence-inference-multiple-choice-ehdwns1516.endpoint.ainize.ai/)
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Sentence Inference Multiple Choice API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/sentence_inference_multiple_choice)
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## Overview
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Language model: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
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Language: English
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Training data: [SWAG dataset](https://huggingface.co/datasets/swag)
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Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/Multiple_choice_SWAG_finetunning)
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## Usage
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## In Transformers
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```
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from transformers import AutoTokenizer, AutoModelForMultipleChoice
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tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
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model = AutoModelForMultipleChoice.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
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def run_model(candicates_count, context: str, candicates: list[str]):
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assert len(candicates) == candicates_count, "you need " + candicates_count + " candidates"
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choices_inputs = []
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for c in candicates:
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text_a = "" # empty context
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text_b = context + " " + c
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inputs = tokenizer(
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text_a,
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text_b,
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add_special_tokens=True,
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max_length=128,
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padding="max_length",
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truncation=True,
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return_overflowing_tokens=True,
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)
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choices_inputs.append(inputs)
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input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
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output = model(input_ids=input_ids)
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return {"result": candicates[torch.argmax(output.logits).item()]}
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items = list()
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count = 4 //candicates count
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context = "your context"
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for i in range(int(count)):
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items.append("sentence")
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result = run_model(count, context, items)
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```
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