roberta base go emotions
简介
核心亮点
- 支持 28 种细粒度情感识别，远超基础正负面分类
- 基于 RoBERTa 架构，推理速度快且部署成本低
- 适用于社交媒体分析、用户反馈洞察等文本挖掘场景
- MIT 开源协议，可快速集成至商业化产品流水线
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
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 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download SamLowe/roberta-base-go_emotions下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download SamLowe/roberta-base-go_emotions config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('SamLowe/roberta-base-go_emotions')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/SamLowe/roberta-base-go_emotions如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/SamLowe/roberta-base-go_emotions模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('SamLowe/roberta-base-go_emotions')
tokenizer = AutoTokenizer.from_pretrained('SamLowe/roberta-base-go_emotions')完整文档
---
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:
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
##### 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
