mt5-small-raw-htr-clean-ver.1.0

Demonstration of fine-tuning of mt5-small for C17th English (and Latin) legal depositions. Uses mt5-small, which is trained on the mC4 common crawl dataset containing 101 languages, including some Latin. mt5-small is the smallest of five variants of mt5 (small; base; large; XL; XXL). Fine-tuned with text to text pairs of raw-HTR and hand-corrected Ground Truth from C17th English High Court of Admiralty depositions.

A series of fine-tuned mt5-small models will be created with ascending version numbers.

Training dataset = 80%; validation dataset = 20%. mt5Tokenizer. PyTorch datasets. T5ForConditionalGeneration model. CER/WER evaluation; Qualitative evaluation (e.g. capitalisation; HTR error correction). Train using Nvidia T4 small 15 GB $0.40/hour.

MT5TOKENIZER Python

from transformers import T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")

TOKENIZE DATA Python

train_encodings = tokenizer(list(train_inputs), text_target=list(train_targets), truncation=True, padding=True) val_encodings = tokenizer(list(val_inputs), text_target=list(val_targets), truncation=True, padding=True)

CREATE PYTORCH DATASETS Python

import torch

class HTRDataset(torch.utils.data.Dataset): def init(self, encodings): self.encodings = encodings

def __getitem__(self, idx):
    return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}

def __len__(self):
    return len(self.encodings.input_ids)  

train_dataset = HTRDataset(train_encodings) val_dataset = HTRDataset(val_encodings)

FINE-TUNING WITH TRANSFORMERS Python

from transformers import T5ForConditionalGeneration

model = T5ForConditionalGeneration.from_pretrained("google/mt5-small")

TRAINING ARGUMENTS: python

training_args = TrainingArguments( output_dir="./results", per_device_train_batch_size=8, # Or 16 if your GPU has enough memory per_device_eval_batch_size=8, # Same as train batch size learning_rate=1e-4, num_train_epochs=3, # Or 5 evaluation_strategy="epoch", save_strategy="epoch", fp16=True, # If your GPU supports it, for faster training # ... other arguments ... )

EARLY STOPPING: python

training_args = TrainingArguments( # ... other arguments ... evaluation_strategy="epoch", load_best_model_at_end=True, metric_for_best_model="eval_loss", early_stopping_patience=3 # Optional )

CREATE TRAINER AND FINE-TUNE Python

from transformers import Trainer

trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset

)

trainer.train()

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Fine-tuning data experiments will include:

• Using 1000 lines of raw-HTR paired with 1000 lines of hand corrected Ground Truth
• Using 2000 lines of raw-HTR paired with 1000 lines of hand corrected Ground Truth
• Using 1000 and 2000 lines of synthetic raw-HTR paired with 1000 lines of handcorrected Ground Truth

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Hyper-parameter experients will include:

• Adjusting batch size from 8 paired-lines to 16 paired-lines
• Adjusting epochs from 3 to 5 epochs
• Adjusting learning rate ** Start with a learning rate of 1e-4 (0.0001). This is a common starting point for fine-tuning transformer models. ** Experiment with slightly higher or lower values (e.g., 5e-4 or 5e-5) in later experiments
• Adjusting earlystopping settings