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xlm-roberta-large-pooled-emotions6

Text Classification
PyTorch
multilingual
xlm-roberta
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metadata
license: mit
language:
  - multilingual
tags:
  - text-classification
  - pytorch
metrics:
  - f1-score
extra_gated_fields:
  Name: text
  Country: country
  Institution: text
  E-mail: text
  Use case: text
extra_gated_prompt: >-
  Our models are intended for academic use only. If you are not affiliated with
  an academic institution, please provide a rationale for using our models.
  Please allow us a few business days to manually review subscriptions.

xlm-roberta-large-pooled-MORES

Model description

An xlm-roberta-large model finetuned on sentence-level 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 7 languages (English, German, French, Polish, Slovak, Czech 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",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

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)

Gated access

Due to the gated access, you must pass the token parameter when loading the model. In earlier versions of the Transformers package, you may need to use the use_auth_token parameter instead.

Model performance

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

Fine-tuning procedure

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

  • Number of Training Epochs: 10
  • Batch Size: 16
  • 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.