santhosh97
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Update README.md
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README.md
CHANGED
@@ -33,7 +33,7 @@ model = AutoModelForCausalLM.from_pretrained('gretelai/text2table').to('cuda', d
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model.eval()
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INSTRUCTION_KEY = "### Instruction: Given the following prompt, generate a table.
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RESPONSE_KEY = "### Response:"
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INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
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PROMPT_FOR_GENERATION_FORMAT = """{intro}
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@@ -43,12 +43,12 @@ PROMPT_FOR_GENERATION_FORMAT = """{intro}
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""".format(
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intro=INTRO_BLURB,
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instruction_key=INSTRUCTION_KEY,
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prompt_to_generate_table="{
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response_key=RESPONSE_KEY,
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)
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PROMPT = "Create a dataset with four columns: patient, sex, agegrp, bp_before and bp_after. The patient column is a numerical identifier, sex is the gender of the patient, agegrp is the age group of the patient, bp_before is the blood pressure (in mmHg) before a certain treatment, and bp_after is the blood pressure (in mmHg) after a certain treatment."
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inputs = PROMPT_FOR_GENERATION_FORMAT.format(
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tokenizer.pad_token = tokenizer.eos_token
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input = tokenizer(inputs, return_tensors="pt").to('cuda')
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input_ids = input['input_ids']
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model.eval()
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+
INSTRUCTION_KEY = "### Instruction: Given the following prompt, generate a table."
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RESPONSE_KEY = "### Response:"
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INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
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PROMPT_FOR_GENERATION_FORMAT = """{intro}
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""".format(
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intro=INTRO_BLURB,
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instruction_key=INSTRUCTION_KEY,
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prompt_to_generate_table="{prompt_to_generate_table}",
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response_key=RESPONSE_KEY,
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)
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PROMPT = "Create a dataset with four columns: patient, sex, agegrp, bp_before and bp_after. The patient column is a numerical identifier, sex is the gender of the patient, agegrp is the age group of the patient, bp_before is the blood pressure (in mmHg) before a certain treatment, and bp_after is the blood pressure (in mmHg) after a certain treatment."
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inputs = PROMPT_FOR_GENERATION_FORMAT.format(prompt_to_generate_table=PROMPT)
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tokenizer.pad_token = tokenizer.eos_token
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input = tokenizer(inputs, return_tensors="pt").to('cuda')
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input_ids = input['input_ids']
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