remi-or

from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaConfig

def distill_roberta(
    teacher_model : RobertaPreTrainedModel,
) -> RobertaPreTrainedModel:
    """
    Distilates a RoBERTa (teacher_model) like would DistilBERT for a BERT model.
    The student model has the same configuration, except for the number of hidden layers, which is // by 2.
    The student layers are initilized by copying one out of two layers of the teacher, starting with layer 0.
    The head of the teacher is also copied.

remi-or

from typing import Any
from transformers import AutoModelForMaskedLM

roberta = AutoModelForMaskedLM.from_pretrained("roberta-large")

def visualize_children(
    object : Any,
    level : int = 0,
) -> None:
    """

remi-or

from transformers import AutoModelForMaskedLM

roberta = AutoModelForMaskedLM.from_pretrained("roberta-large")

print(roberta)

remi-or

import matplotlib.pyplot as plt
import seaborn as sns

def average_word_count(list_of_texts):
    """
    Returns the average word count of a list of texts.
    """
    total_count = 0
    for text in list_of_texts:
        text = text.replace("'", ' ')

remi-or

# This snippet requires you to install Hugging Face's datasets module
from datasets import load_dataset
import pandas as pd

Dataframe = pd.DataFrame({})

questions = load_dataset('squad')['train']['question'][:3000]
Dataframe = Dataframe.append(pd.DataFrame({'Text' : questions, 'Source' : 'squad'}))

questions = load_dataset('hotpot_qa', 'distractor')['train']['question'][:3000]