I have dataset with two column: one with faulty addresses, and other with correct addresses. I want to train a model such that, I can use it later for correcting all the incoming faulty addresses. I have done tokenization, data splitting task for the same, but I can't make my model to start training.
I get GraphExecution error, which points towrads mismatch of dimension.
Exact error: logits and labels must have the same first dimension, got logits shape [16,55] and labels shape [880]
I don't know from where these values are coming from, as my X_Train consists of list with each list being length of 45, and y_train is a list of length 55. (All are integer values)
As, I am new to this task, any suggestions, comments, concerns, questions are welcome.
Also, I have tried basic ML approach, but the results were quite poor. Hence, please suggest me on the line of this approach only.
Here are some specifications to my approach:
Tokenization: RobertaTokenizer.from_pretrained("roberta-base")
Model: TFRobertaForSequenceClassification.from_pretrained("roberta-base")
optimizer: Adam
learning_rate = 1e-5
loss: SparseCategoricalCrossentropy
epochs: 10
batch_size: 16
I think following are the list of models which we can use:
github link used: https://github.com/huggingface/transformers/blob/main/src/transformers/models/roberta/__init__.py
TIA
