What is ConvBERT model in transformers?

This recipe explains what is ConvBERT model in transformers.

Recipe Objective: What is ConvBERT model in transformers?

The bare ConvBERT Model transformer, with no special head on top, outputs raw hidden-states. This model is a subclass of PyTorch torch.nn.Module. Use it like any other PyTorch Module, and refer to the PyTorch documentation for for questions about general behaviour and usage.

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Example of ConvBert model-

# Importing required libraries
from transformers import ConvBertTokenizer, ConvBertModel
import torch

# Load the tokenizer and model of the "conv-bert-base" pretrained model
tz = ConvBertTokenizer.from_pretrained('YituTech/conv-bert-base')
convbert_model = ConvBertModel.from_pretrained('YituTech/conv-bert-base')

#Tokenizing the input data and assigning the token their IDs
input_values = tz("The quick brown fox jumps over the lazy dog fox", return_tensors="pt")
output_values = convbert_model(**input_values)

##last_hidden_state contains the sequence of hidden-states at the output of the last layer of the model.
last_hidden_states = output_values.last_hidden_state

#displaying the hidden-states
print("last hidden states: ",last_hidden_states)

Output -
last hidden states:  tensor([[[ 0.0085, -0.3978, -0.0495,  ...,  0.6039, -0.6970,  0.8964],
         [ 0.5598,  0.3251, -0.1915,  ..., -0.0461,  0.1663, -0.6888],
         [ 0.2261,  0.4016,  0.0646,  ..., -0.0855, -0.0150, -0.1862],
         ...,
         [ 0.0623,  0.0270, -0.0317,  ..., -0.0983, -0.1265, -0.2174],
         [ 0.3074,  0.7127, -0.1888,  ..., -0.6748, -0.2734, -0.7965],
         [-0.1279, -0.5092, -0.6420,  ...,  0.9400, -0.6222,  1.4377]]],
       grad_fn=)

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