Text Classification
Transformers
Safetensors
English
roberta
emotion
emotions
multi-class-classification
multi-label-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use minuva/MiniLMv2-goemotions-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minuva/MiniLMv2-goemotions-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minuva/MiniLMv2-goemotions-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minuva/MiniLMv2-goemotions-v2") model = AutoModelForSequenceClassification.from_pretrained("minuva/MiniLMv2-goemotions-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from minuva/MiniLMv2-goemotions-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/minuva/MiniLMv2-goemotions-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://minuva/MiniLMv2-goemotions-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/minuva/MiniLMv2-goemotions-v2/resolve/main/tokenizer.json
2.11 MB
File too large to display, you can check the raw version instead.