Name: roberta base go emotions
Author: SamLowe

Overview

The roberta-base-go-emotions model is a specialized text classifier fine-tuned on the GoEmotions dataset to detect nuanced emotional states in short-form text. Unlike basic sentiment analysis that merely categorizes input as positive or negative, this model distinguishes between 28 distinct emotion categories, making it ideal for developers building empathetic chatbots, social media monitoring tools, or customer feedback loops. Built on the RoBERTa architecture, it offers a strong balance between inference speed and contextual accuracy. Integration is straightforward via the Hugging Face Transformers library, allowing for rapid deployment into existing Python-based NLP pipelines without the need for extensive custom training.

Highlights

Classifies 28 distinct emotional states beyond basic sentiment
Fine-tuned on the comprehensive GoEmotions dataset
Built on the robust RoBERTa-base transformer architecture
Easy integration via Hugging Face Transformers library
Optimized for short-form text and social media analysis

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("SamLowe/roberta-base-go_emotions")
tokenizer = AutoTokenizer.from_pretrained("SamLowe/roberta-base-go_emotions")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download SamLowe/roberta-base-go_emotions

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download SamLowe/roberta-base-go_emotions config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('SamLowe/roberta-base-go_emotions')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/SamLowe/roberta-base-go_emotions

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/SamLowe/roberta-base-go_emotions

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('SamLowe/roberta-base-go_emotions')
tokenizer = AutoTokenizer.from_pretrained('SamLowe/roberta-base-go_emotions')

Full Documentation

来源: HuggingFace

---
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.

---

#### 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:

python

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/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb

![Open In Colab](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