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metadata
language:
  - th
  - en
license: mit
base_model: aisingapore/sea-lion-7b-instruct
datasets:
  - AIAT/Optimizer-datasetfinal
pipeline_tag: text-generation

Sea-lion2pandas

fine-tuned from sea-lion-7b-instruct with question-pandas expression pairs.

How to use:

from transformers import AutoModelForCausalLM, AutoTokenizer
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("AIAT/Optimizer-sealion2pandas", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AIAT/Optimizer-sealion2pandas", trust_remote_code=True)

df = pd.read_csv("Your csv..")

prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n"

prompt = """\
You are working with a pandas dataframe in Python. 
The name of the dataframe is `df`.
This is the result of `print(df.head())`:
{df_str}

Follow these instructions: 
1. Convert the query to executable Python code using Pandas. 
2. The final line of code should be a Python expression that can be called with the `eval()` function.
3. The code should represent a solution to the query.
4. PRINT ONLY THE EXPRESSION.
5. Do not quote the expression.
Query: {query_str} """

def create_prompt(query_str, df):
    text = prompt.format(df_str=str(df.head()), query_str=query_str)
    text = prompt_template.format(human_prompt=text)
    return text

full_prompt = create_prompt("Find test ?", df)

tokens = tokenizer(full_prompt, return_tensors="pt")
output = model.generate(tokens["input_ids"], max_new_tokens=20, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(output[0], skip_special_tokens=True))

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