xlm-roberta-large-pooled-MORES

Model description

An xlm-roberta-large model finetuned on multilingual training data hand-annotated using the following labels:

• 0: "Anger"
• 1: "Fear"
• 2: "Disgust"
• 3: "Sadness"
• 4: "Joy"
• 5: "None of Them"

This model can also be used for sentiment classification with the following conversion:

• Joy (4) → Positive
• None of Them (5) → Neutral (or None of Them)
• All Other Labels → Negative

The training data we used was augmented using artificially generated examples and translated texts. It covers 5 languages (English, German, French, Polish, and Hungarian) with nearly identical shares.

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(model="poltextlab/xlm-roberta-large-pooled-MORES", tokenizer=tokenizer, use_fast=False)

text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities."
pipe(text)

Model performance

The model was evaluated on language-specific test sets and demonstrated nearly identical performance across all languages:

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 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 coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the Babel Machine.

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.