Text Classification
Transformers
Safetensors
English
bert
emotion
classification
emojis
emotions
v1.0
sentiment-analysis
nlp
lightweight
chatbot
social-media
mental-health
short-text
emotion-detection
real-time
expressive
ai
machine-learning
english
inference
edge-ai
smart-replies
tone-analysis
Instructions to use Varnikasiva/sentiment-classification-bert-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Varnikasiva/sentiment-classification-bert-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Varnikasiva/sentiment-classification-bert-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Varnikasiva/sentiment-classification-bert-mini") model = AutoModelForSequenceClassification.from_pretrained("Varnikasiva/sentiment-classification-bert-mini") - Inference
- Notebooks
- Google Colab
- Kaggle
labels
#1
by fabras - opened
Hi there,
the labels are just LABEL_0,LABEL_1,LABEL_2 and so on. Why you didn't use english words?
Hi there,
the labels are just LABEL_0,LABEL_1,LABEL_2 and so on. Why you didn't use english words?
Updating !
Varnikasiva changed discussion status to closed