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--- |
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language: |
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- th |
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- en |
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license: mit |
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base_model: aisingapore/sea-lion-7b-instruct |
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datasets: |
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- AIAT/Optimizer-datasetfinal |
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pipeline_tag: text-generation |
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--- |
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## Sea-lion2pandas |
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fine-tuned from [sea-lion-7b-instruct](aisingapore/sea-lion-7b-instruct) with question-pandas expression pairs. |
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## How to use: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import pandas as pd |
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tokenizer = AutoTokenizer.from_pretrained("AIAT/Optimizer-sealion2pandas", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("AIAT/Optimizer-sealion2pandas", trust_remote_code=True) |
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df = pd.read_csv("Your csv..") |
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prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n" |
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prompt = """\ |
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You are working with a pandas dataframe in Python. |
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The name of the dataframe is `df`. |
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This is the result of `print(df.head())`: |
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{df_str} |
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Follow these instructions: |
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1. Convert the query to executable Python code using Pandas. |
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2. The final line of code should be a Python expression that can be called with the `eval()` function. |
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3. The code should represent a solution to the query. |
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4. PRINT ONLY THE EXPRESSION. |
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5. Do not quote the expression. |
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Query: {query_str} """ |
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def create_prompt(query_str, df): |
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text = prompt.format(df_str=str(df.head()), query_str=query_str) |
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text = prompt_template.format(human_prompt=text) |
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return text |
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full_prompt = create_prompt("Find test ?", df) |
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tokens = tokenizer(full_prompt, return_tensors="pt") |
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output = model.generate(tokens["input_ids"], max_new_tokens=20, eos_token_id=tokenizer.eos_token_id) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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# sponser |
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