𝐄𝐦𝐨𝐑𝐨𝐁𝐄𝐑𝐓𝐚 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐬 𝟐𝟕 𝐞𝐦𝐨𝐭𝐢𝐨𝐧𝐬 𝐨𝐫 𝐧𝐞𝐮𝐭𝐫𝐚𝐥 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐭𝐞𝐱𝐭.

It’s now around 160k downloads per month :’)

RoBERTa + go_emotions = 𝐄𝐦𝐨𝐑𝐨𝐁𝐄𝐑𝐓𝐚

My first model on Hugging Face (An open-source AI platform). 🤗

Have a try on this link! (Interactive-link)

https://huggingface.co/arpanghoshal/EmoRoBERTa

Check out how your text feels. 👻

Want me to write on how I created this model? Like this post!

Description

What is GoEmotions?

Dataset labelled 58000 Reddit comments with 28 emotions

• admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise + neutral

What is RoBERTa?

• RoBERTa builds on BERT’s language masking strategy and modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time. This allows RoBERTa representations to generalize even better to downstream tasks compared to BERT.

Hyperparameters

• ParameterLearning rate: 5e-5

• Epochs: 10

• Max Seq Length: 50

• Batch size: 16

• Warmup Proportion: 0.1

• Epsilon: 1e-8

Results

Best Result of Macro F1 - 49.30%

Usage

from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline

tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa")

emotion = pipeline('sentiment-analysis', 
                    model='arpanghoshal/EmoRoBERTa')

emotion_labels = emotion("Thanks for using it.")
print(emotion_labels)

Output

[{'label': 'gratitude', 'score': 0.9964383244514465}]