xlm-roberta-large-budget-cap-v3

Model description

An xlm-roberta-large model finetuned on multilingual training data containing texts of the budget domain labelled with major topic codes from the Comparative Agendas Project.

How to use the model

Loading and tokenizing input data

import pandas as pd
import numpy as np
from datasets import Dataset
from transformers import (AutoModelForSequenceClassification, AutoTokenizer, 
                          Trainer, TrainingArguments)

CAP_NUM_DICT = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6', 
6: '7', 7: '8', 8: '9', 9: '10', 10: '12', 11: '13', 12: '14', 
13: '15', 14: '16', 15: '17', 16: '18', 17: '19', 18: '20', 19: 
'21', 20: '23', 21: '999'}

tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-large')
num_labels = len(CAP_NUM_DICT)

def tokenize_dataset(data : pd.DataFrame):
    tokenized = tokenizer(data["text"],
                          max_length=MAXLEN,
                          truncation=True,
                          padding="max_length")
    return tokenized

hg_data = Dataset.from_pandas(data)
dataset = hg_data.map(tokenize_dataset, batched=True, remove_columns=hg_data.column_names)

Inference using the Trainer class

model = AutoModelForSequenceClassification.from_pretrained('poltextlab/xlm-roberta-large-budget-cap-v3',
                                                           num_labels=22,
                                                           problem_type="multi_label_classification",
                                                           ignore_mismatched_sizes=True
                                                           )

training_args = TrainingArguments(
    output_dir='.',
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8
)

trainer = Trainer(
    model=model,
    args=training_args
)

probs = trainer.predict(test_dataset=dataset).predictions
predicted = pd.DataFrame(np.argmax(probs, axis=1)).replace({0: CAP_NUM_DICT}).rename(
    columns={0: 'predicted'}).reset_index(drop=True)

Fine-tuning procedure

xlm-roberta-large-budget-cap-v3 was fine-tuned using the Hugging Face Trainer class with the following hyperparameters:

training_args = TrainingArguments(
    output_dir=f"../model/{model_dir}/tmp/",
    logging_dir=f"../logs/{model_dir}/",
    logging_strategy='epoch',
    num_train_epochs=10,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    learning_rate=5e-06,
    seed=42,
    save_strategy='epoch',
    evaluation_strategy='epoch',
    save_total_limit=1,
    load_best_model_at_end=True
)

We also incorporated an EarlyStoppingCallback in the process with a patience of 2 epochs.

Model performance

The model was evaluated on a test set of 13707 examples (10% of the available data).
Model accuracy is 0.99.

label	precision	recall	f1-score	support
0	0.98	0.97	0.98	548
1	0.99	0.98	0.99	370
2	0.99	0.99	0.99	938
3	1	1	1	509
4	0.97	0.99	0.98	722
5	0.99	0.99	0.99	1175
6	0.99	0.99	0.99	435
7	0.99	0.99	0.99	206
8	1	1	1	60
9	0.99	0.99	0.99	470
10	0.99	0.99	0.99	1329
11	0.98	0.97	0.98	698
12	0.97	0.98	0.97	540
13	0.99	0.99	0.99	1008
14	1	1	1	475
15	1	1	1	814
16	0.99	1	1	134
17	0.99	0.99	0.99	602
18	0.99	0.98	0.98	1646
19	0.99	0.98	0.99	635
20	0.99	0.99	0.99	364
21	0.96	0.93	0.95	29
macro avg	0.99	0.99	0.99	13707
weighted avg	0.99	0.99	0.99	13707

Inference platform

This model is used by the CAP Babel Machine, an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.

Cooperation

Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the CAP Babel Machine.

Debugging and issues

This architecture uses the sentencepiece tokenizer. In order to run the model before transformers==4.27 you need to install it manually.

If you encounter a RuntimeError when loading the model using the from_pretrained() method, adding ignore_mismatched_sizes=True should solve the issue.