roberta-base-go_emotions EU AI Act Compliance Profile

Overview

Model trained from roberta-base on the go_emotions dataset for multi-label classification.

ONNX version also available

A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx. These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference.

Dataset used for the model

go_emotions is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label.

How the model was created

The model was trained using AutoModelForSequenceClassification.from_pretrained with problem_type="multi_label_classification" for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01.

Inference

There are multiple ways to use this model in Huggingface Transformers. Possibly the simplest is using a pipeline:

from transformers import pipeline

classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)

sentences = ["I am not having a great day"]

model_outputs = classifier(sentences)
print(model_outputs[0])
# produces a list of dicts for each of the labels

Evaluation / metrics

Evaluation of the model is available at

• https://github.com/samlo

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