metadata

language:
  - en
tags:
  - text-classification
  - pytorch
metrics:
  - accuracy
  - f1-score
extra_gated_prompt: >-
  Please provide some basic information that confirms the scholarly use of our
  models.

  If you use our models for your work or research, please cite this paper:
  Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging
  Open Large Language Models for Multilingual Policy Topic Classification: The
  Babel Machine Approach. Social Science Computer Review, 0(0).
  https://doi.org/10.1177/08944393241259434
extra_gated_fields:
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xlm-roberta-large-english-execspeech-cap-v3

Model description

An xlm-roberta-large model fine-tuned on english training data containing executive speeches labeled with major topic codes from the Comparative Agendas Project.

How to use the model

This snippet prints the three most probable labels and their corresponding softmax scores:

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("poltextlab/xlm-roberta-large-english-execspeech-cap-v3")
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")

sentence = "This is an example."

inputs = tokenizer(sentence,
                   return_tensors="pt",
                   max_length=512,
                   padding="do_not_pad",
                   truncation=True
                   )

logits = model(**inputs).logits

probs = torch.softmax(logits, dim=1).tolist()[0]
probs = {model.config.id2label[index]: round(probability, 2) for index, probability in enumerate(probs)}
top3_probs = dict(sorted(probs.items(), key=lambda item: item[1], reverse=True)[:3])

print(top3_probs)

Model performance

The model was evaluated on a test set of 16907 examples.
Model accuracy is 0.71.

label	precision	recall	f1-score	support
0	0.69	0.75	0.72	1731
1	0.61	0.5	0.55	278
2	0.83	0.79	0.81	685
3	0.73	0.75	0.74	209
4	0.65	0.5	0.56	347
5	0.81	0.8	0.81	694
6	0.71	0.62	0.66	176
7	0.81	0.76	0.79	304
8	0.77	0.6	0.67	120
9	0.8	0.67	0.73	196
10	0.75	0.68	0.71	577
11	0.67	0.63	0.65	435
12	0.66	0.57	0.61	203
13	0.69	0.55	0.61	322
14	0.71	0.64	0.67	997
15	0.58	0.69	0.63	159
16	0.66	0.62	0.64	327
17	0.67	0.75	0.71	1626
18	0.6	0.64	0.62	770
19	0.55	0.58	0.56	251
20	0.8	0.82	0.81	5324
21	0.46	0.46	0.46	1176
macro avg	0.69	0.65	0.67	16907
weighted avg	0.71	0.71	0.71	16907

Fine-tuning procedure

This model was fine-tuned with the following key hyperparameters:

Number of Training Epochs: 10
Batch Size: 8
Learning Rate: 5e-06
Early Stopping: enabled with a patience of 2 epochs

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.

Reference

Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434

Debugging and issues

This architecture uses the sentencepiece tokenizer. In order to use 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.