README.md

---
language: en
tags:
- text-classification
- pytorch
- roberta
- emotions
- multi-class-classification
- multi-label-classification
datasets:
- go_emotions
license: mit
widget:
- text: I am not having a great day.
library_name: transformers.js
---

# This is a Transformers.js clone of [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) ! 

#### Overview

Model trained from [roberta-base](https://huggingface.co/roberta-base) on the [go_emotions](https://huggingface.co/datasets/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](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](https://huggingface.co/datasets/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:

```js
const { pipeline } = await import('@xenova/transformers');

// Allocate pipeline
const pipe = await pipeline('text-classification', "MicahB/roberta-base-go_emotions");
console.log(await pipe("I love transformers!"));
```

```js
Output:
[ { label: 'love', score: 0.9529242515563965 } ]
```

#### Evaluation / metrics

Evaluation of the model is available at

- https://github.com/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb)

##### Summary

As provided in the above notebook, evaluation of the multi-label output (of the 28 dim output via a threshold of 0.5 to binarize each) using the dataset test split gives:

- Accuracy: 0.474
- Precision: 0.575
- Recall: 0.396
- F1: 0.450

But the metrics are more meaningful when measured per label given the multi-label nature (each label is effectively an independent binary classification) and the fact that there is drastically different representations of the labels in the dataset.

With a threshold of 0.5 applied to binarize the model outputs, as per the above notebook, the metrics per label are:

| | accuracy | precision | recall | f1 | mcc | support | threshold |
| -------------- | -------- | --------- | ------ | ----- | ----- | ------- | --------- |
| admiration | 0.946 | 0.725 | 0.675 | 0.699 | 0.670 | 504 | 0.5 |
| amusement | 0.982 | 0.790 | 0.871 | 0.829 | 0.821 | 264 | 0.5 |
| anger | 0.970 | 0.652 | 0.379 | 0.479 | 0.483 | 198 | 0.5 |
| annoyance | 0.940 | 0.472 | 0.159 | 0.238 | 0.250 | 320 | 0.5 |
| approval | 0.942 | 0.609 | 0.302 | 0.404 | 0.403 | 351 | 0.5 |
| caring | 0.973 | 0.448 | 0.319 | 0.372 | 0.364 | 135 | 0.5 |
| confusion | 0.972 | 0.500 | 0.431 | 0.463 | 0.450 | 153 | 0.5 |
| curiosity | 0.950 | 0.537 | 0.356 | 0.428 | 0.412 | 284 | 0.5 |
| desire | 0.987 | 0.630 | 0.410 | 0.496 | 0.502 | 83 | 0.5 |
| disappointment | 0.974 | 0.625 | 0.199 | 0.302 | 0.343 | 151 | 0.5 |
| disapproval | 0.950 | 0.494 | 0.307 | 0.379 | 0.365 | 267 | 0.5 |
| disgust | 0.982 | 0.707 | 0.333 | 0.453 | 0.478 | 123 | 0.5 |
| embarrassment | 0.994 | 0.750 | 0.243 | 0.367 | 0.425 | 37 | 0.5 |
| excitement | 0.983 | 0.603 | 0.340 | 0.435 | 0.445 | 103 | 0.5 |
| fear | 0.992 | 0.758 | 0.603 | 0.671 | 0.672 | 78 | 0.5 |
| gratitude | 0.990 | 0.960 | 0.881 | 0.919 | 0.914 | 352 | 0.5 |
| grief | 0.999 | 0.000 | 0.000 | 0.000 | 0.000 | 6 | 0.5 |
| joy | 0.978 | 0.647 | 0.559 | 0.600 | 0.590 | 161 | 0.5 |
| love | 0.982 | 0.773 | 0.832 | 0.802 | 0.793 | 238 | 0.5 |
| nervousness | 0.996 | 0.600 | 0.130 | 0.214 | 0.278 | 23 | 0.5 |
| optimism | 0.972 | 0.667 | 0.376 | 0.481 | 0.488 | 186 | 0.5 |
| pride | 0.997 | 0.000 | 0.000 | 0.000 | 0.000 | 16 | 0.5 |
| realization | 0.974 | 0.541 | 0.138 | 0.220 | 0.264 | 145 | 0.5 |
| relief | 0.998 | 0.000 | 0.000 | 0.000 | 0.000 | 11 | 0.5 |
| remorse | 0.991 | 0.553 | 0.750 | 0.636 | 0.640 | 56 | 0.5 |
| sadness | 0.977 | 0.621 | 0.494 | 0.550 | 0.542 | 156 | 0.5 |
| surprise | 0.981 | 0.750 | 0.404 | 0.525 | 0.542 | 141 | 0.5 |
| neutral | 0.782 | 0.694 | 0.604 | 0.646 | 0.492 | 1787 | 0.5 |

Optimizing the threshold per label for the one that gives the optimum F1 metrics gives slightly better metrics - sacrificing some precision for a greater gain in recall, hence to the benefit of F1 (how this was done is shown in the above notebook):

| | accuracy | precision | recall | f1 | mcc | support | threshold |
| -------------- | -------- | --------- | ------ | ----- | ----- | ------- | --------- |
| admiration | 0.940 | 0.651 | 0.776 | 0.708 | 0.678 | 504 | 0.25 |
| amusement | 0.982 | 0.781 | 0.890 | 0.832 | 0.825 | 264 | 0.45 |
| anger | 0.959 | 0.454 | 0.601 | 0.517 | 0.502 | 198 | 0.15 |
| annoyance | 0.864 | 0.243 | 0.619 | 0.349 | 0.328 | 320 | 0.10 |
| approval | 0.926 | 0.432 | 0.442 | 0.437 | 0.397 | 351 | 0.30 |
| caring | 0.972 | 0.426 | 0.385 | 0.405 | 0.391 | 135 | 0.40 |
| confusion | 0.974 | 0.548 | 0.412 | 0.470 | 0.462 | 153 | 0.55 |
| curiosity | 0.943 | 0.473 | 0.711 | 0.568 | 0.552 | 284 | 0.25 |
| desire | 0.985 | 0.518 | 0.530 | 0.524 | 0.516 | 83 | 0.25 |
| disappointment | 0.974 | 0.562 | 0.298 | 0.390 | 0.398 | 151 | 0.40 |
| disapproval | 0.941 | 0.414 | 0.468 | 0.439 | 0.409 | 267 | 0.30 |
| disgust | 0.978 | 0.523 | 0.463 | 0.491 | 0.481 | 123 | 0.20 |
| embarrassment | 0.994 | 0.567 | 0.459 | 0.507 | 0.507 | 37 | 0.10 |
| excitement | 0.981 | 0.500 | 0.417 | 0.455 | 0.447 | 103 | 0.35 |
| fear | 0.991 | 0.712 | 0.667 | 0.689 | 0.685 | 78 | 0.40 |
| gratitude | 0.990 | 0.957 | 0.889 | 0.922 | 0.917 | 352 | 0.45 |
| grief | 0.999 | 0.333 | 0.333 | 0.333 | 0.333 | 6 | 0.05 |
| joy | 0.978 | 0.623 | 0.646 | 0.634 | 0.623 | 161 | 0.40 |
| love | 0.982 | 0.740 | 0.899 | 0.812 | 0.807 | 238 | 0.25 |
| nervousness | 0.996 | 0.571 | 0.348 | 0.432 | 0.444 | 23 | 0.25 |
| optimism | 0.971 | 0.580 | 0.565 | 0.572 | 0.557 | 186 | 0.20 |
| pride | 0.998 | 0.875 | 0.438 | 0.583 | 0.618 | 16 | 0.10 |
| realization | 0.961 | 0.270 | 0.262 | 0.266 | 0.246 | 145 | 0.15 |
| relief | 0.992 | 0.152 | 0.636 | 0.246 | 0.309 | 11 | 0.05 |
| remorse | 0.991 | 0.541 | 0.946 | 0.688 | 0.712 | 56 | 0.10 |
| sadness | 0.977 | 0.599 | 0.583 | 0.591 | 0.579 | 156 | 0.40 |
| surprise | 0.977 | 0.543 | 0.674 | 0.601 | 0.593 | 141 | 0.15 |
| neutral | 0.758 | 0.598 | 0.810 | 0.688 | 0.513 | 1787 | 0.25 |

This improves the overall metrics:

- Precision: 0.542
- Recall: 0.577
- F1: 0.541

Or if calculated weighted by the relative size of the support of each label:

- Precision: 0.572
- Recall: 0.677
- F1: 0.611

#### Commentary on the dataset

Some labels (E.g. gratitude) when considered independently perform very strongly with F1 exceeding 0.9, whilst others (E.g. relief) perform very poorly.

This is a challenging dataset. Labels such as relief do have much fewer examples in the training data (less than 100 out of the 40k+, and only 11 in the test split).

But there is also some ambiguity and/or labelling errors visible in the training data of go_emotions that is suspected to constrain the performance. Data cleaning on the dataset to reduce some of the mistakes, ambiguity, conflicts and duplication in the labelling would produce a higher performing model.