transformers/model_cards/mrm8488/distilroberta-base-finetuned-sentiment

DistilRoBERTa + Sentiment Analysis 😂😢😡😃😯

This in an adapted version of @omarsar0 tutorial He explains everything so detailed and provided the dataset. I just changed some parameters and created the config.jsonfile to upload it to 🤗Transformers HUB

In this tutorial, he shows how to fine-tune a language model (LM) for emotion classification with code adapted from this tutorial by MARCIN ZABŁOCKI.

The emotions covered are:

• sadness 😢
• joy 😃
• love 🥰
• anger 😡
• fear 😱
• surprise 😯

Details of the language model

The base model used is DistilRoBERTa

Details of the downstream task (Sentence classification) - Dataset 📚

Dataset split	# Size	# Sequences
Train	1.58M	20000
Validation	200 KB
Test	202 KB

Results after training 🏋️‍♀️🧾

emotion	precision	recall	f1-score	support
sadness	0.973868	0.949066	0.961307	589
joy	0.970313	0.901306	0.934537	689
love	0.743119	0.925714	0.824427	175
anger	0.884615	0.969349	0.925046	261
fear	0.951456	0.875000	0.911628	224
surprise	0.750000	0.919355	0.826087	62
accuracy			0.924000	2000
macro avg	0.878895	0.923298	0.897172	2000
weighted avg	0.931355	0.924000	0.925620	2000

Model in action 🔨

Fast usage with pipelines 🧪

from transformers import pipeline

nlp_sentiment = pipeline(
    "sentiment-analysis",
    model="mrm8488/distilroberta-base-finetuned-sentiment",
    tokenizer="mrm8488/distilroberta-base-finetuned-sentiment"
)

text = "i feel i should return to the start of the weekend so my loyal readers can get a feeling for things up to this point"

nlp_sentiment(text)
# Output: [{'label': 'love', 'score': 0.2183746}]

Created by Manuel Romero/@mrm8488

Made with ♥ in Spain