kasperjunge

from pydantic import BaseModel, Field
import json

class ReceiptDataExtractor(BaseModel):
    date: str = Field(description="The date.")
    amount: float = Field(description="The total amount.")
    supplier: str = Field(description="The supplier.")

    class Config:
        function_meta = {

kasperjunge

import pandas as pd

def chunk_dataframe(df: pd.DataFrame, chunk_size: int) -> pd.DataFrame:
    for i in range(0, len(df), chunk_size):
        yield df[i:i+chunk_size]

kasperjunge

from fastapi import FastAPI
import transformers
import torch

app = FastAPI()

# Load the tokenizer and model
tokenizer = transformers.AutoTokenizer.from_pretrained("model_name")
model = transformers.AutoModelForTokenClassification.from_pretrained("model_name")

kasperjunge

import time
import statistics 
from typing import Callable, Any, Tuple

def time_function(func: Callable, func_input: Any, n_runs: int) -> Tuple[float]:
    times = []
    for _ in range(n_runs):
        start = time.perf_counter()
        func(func_input)
        times.append(time.perf_counter() - start)

kasperjunge

from transformers import AutoTokenizer

# define sample text
text = "Rødgrød med fløde."

# init tokenizer
model_id = "Maltehb/danish-bert-botxo"
tokenizer = AutoTokenizer.from_pretrained(model_id)

# encode text

kasperjunge

from typing import Union
from transformers import AutoTokenizer
from datasets import Dataset, DatasetDict

def tokenize_huggingface_dataset(
    ds: Union[Dataset, DatasetDict], 
    tokenizer: AutoTokenizer,
    max_length: int = 512,
    truncation: bool = True,
) -> Union[Dataset, DatasetDict]:

kasperjunge

import pandas as pd
from datasets import Dataset, DatasetDict

def dataframe_to_huggingface_dataset(df: pd.DataFrame) -> DatasetDict:
    """Convert at dataframe with split column into Hugging Face DatasetDict.

    Args:
        df (pd.DataFrame): Dataset stored in a pandas dataframe with a 
        column named "split" that tells what split a datapoint belongs to.
        

kasperjunge

import pandas as pd
from transformers import pipeline

text = """
Dan Saattrup Nielsen arbejder som AI Specialist hos Alexandra Instituttet
og er han næstformand i Dansk Data Science Community.
"""

ner = pipeline(
    task="ner",                

kasperjunge

from typing import List, Any

def flatten_list(list_of_lists: List[List[Any]]) -> List[Any]:
    """Merge/flatten a list of lists into one single list.
    Example: [[1, 2, 3],[4, 5, 6]] --> [1, 2, 3, 4, 5, 6]

    Args:
        list_of_lists (List[list]): List of lists to be merged/flattened. 

    Returns:

kasperjunge

import pandas as pd
from tqdm import tqdm
from datasets import load_dataset

def download_n_eb_articles(n: int) -> pd.DataFrame:
    """Extract n Ekstra Bladet articles from the Danish subset
    of the mC4 dataset.
    Args:
        n (int): Number of articles to extract.
    Returns: