be-tiny-bart
A model for lemmatisation of Belarusian, trained on Belarusian-HSE dataset.
Model Details
Model Description
- Developed by: Ilia Afanasev
- Model type: BART
- Language(s) (NLP): Belarusian
- License: mpl-2.0
- Finetuned from model: sshleifer/bart-tiny-random
Model Sources
- Paper: TBP
Uses
Sequence-to-sequence transformation.
Direct Use
The system was fine-tuned for lemmatisation of Modern Standard Belarusian.
Out-of-Scope Use
Downstream use and further fine-tuning (for instance, for text-to-SQL transformation) seem to be
Bias, Risks, and Limitations
The model is fine-tuned only for Modern Standard Belarusian on a rather small Belarusian-HSE dataset. Use its results only after the manual check.
[More Information Needed]
Recommendations
Use this model only for lemmatisation of Modern Standard Belarusian if you aspire for the reliable silver tagging results. Any kind of regional, territorial or social variation is going to lead to the catastrophic forgetting issues.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
Training Procedure
Virtual environment:
- Python 3.10.12
- Transformers 4.34.0
- sentence-splitter==1.4
- simpletransformers==0.64.3
- stanza==1.8.1
- torch==2.1.0
The script:
import logging
import pandas as pd
from simpletransformers.seq2seq import Seq2SeqModel
import argparse
import torch
import random
def load_conllu_dataset(datafile):
arr = []
with open(datafile, encoding='utf-8') as inp:
strings = inp.readlines()
for s in strings:
if (s[0] != "#" and s.strip()):
split_string = s.split('\t')
arr.append([split_string[1] + " " + split_string[3]+ " " + split_string[5], split_string[2]])
return pd.DataFrame(arr, columns=["input_text", "target_text"])
def count_matches(labels, preds):
print(labels)
print(preds)
return sum([1 if label == pred else 0 for label, pred in zip(labels, preds)])
def main(args):
train_df = load_conllu_dataset(args.train_data)
args.fraction = float(args.fraction)
print(f'Loading training dataset of {train_df.shape[0]} tokens')
eval_df = load_conllu_dataset(args.dev_data)
random.seed(int(args.seed))
print(f'Setting seed to {args.seed}')
if args.fraction > 0.0 and args.fraction < 1.0:
remainder = int(args.fraction * len(train_df))
train_df = train_df.sample(remainder)
print(f'Subsampling training dataset to {train_df.shape[0]} tokens')
model_args = {
"reprocess_input_data": True,
"overwrite_output_dir": True,
"max_seq_length": max([len(token) for token in train_df["target_text"].tolist()]),
"train_batch_size": int(args.batch),
"num_train_epochs": int(args.epochs),
"save_eval_checkpoints": False,
"save_model_every_epoch": False,
# "silent": True,
"evaluate_generated_text": False,
"evaluate_during_training": False,
"evaluate_during_training_verbose": False,
"use_multiprocessing": False,
"use_multiprocessing_for_evaluation": False,
"save_best_model": False,
"max_length": max([len(token) for token in train_df["input_text"].tolist()]),
"save_steps": -1,
}
model = Seq2SeqModel(
encoder_decoder_type=args.model_type,
encoder_decoder_name=args.model,
args=model_args,
use_cuda = torch.cuda.is_available(),)
model.train_model(train_df, eval_data=eval_df, matches=count_matches)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train_data')
parser.add_argument('--dev_data')
parser.add_argument('--model_type', default="bart")
parser.add_argument('--model', default="tiny-bart")
parser.add_argument('--epochs', default="2")
parser.add_argument('--batch', default="4")
parser.add_argument('--fraction', help="Fraction of data", default=1.0)
parser.add_argument('--seed', help="random seed", default=1590)
args = parser.parse_args()
main(args)
Training Hyperparameters
- Training regime: fp32
- Epochs: 2
- Batch: 7
- Seed: 1590
Speeds, Sizes, Times
The training took around 2.5 hrs on 4 GB GPU (NVIDIA GeForce RTX 3050).
Evaluation
During the training, no implementation procedures were introduced.
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
- Hardware Type: Personal laptop (Xiaomi Mi Notebook Pro X 15)
- Hours used: 4h
- Carbon emitted: approx. 0.1 kg.
Technical Specifications [optional]
Model Architecture and Objective
- Architecture: BART
- Objective: sequence-to-sequence transformation
Compute Infrastructure
Personal laptop
Hardware
- Xiaomi Mi Notebook Pro X 15
Software
- VS Code
Citation
BibTeX:
TBP
APA:
TBP
Model Card Authors [optional]
Ilia Afanasev