Text Classification
Transformers
Safetensors
Chinese
bert
agent
nlp
chinese
sentiment-analysis
emotion
regression
vad
valence-arousal-dominance
macbert
text-embeddings-inference
Instructions to use Pectics/vad-macbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pectics/vad-macbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pectics/vad-macbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pectics/vad-macbert") model = AutoModelForSequenceClassification.from_pretrained("Pectics/vad-macbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70586e25268dc02bb716463c1a70caeecfddba80b12651eab94695189b429620
- Size of remote file:
- 409 MB
- SHA256:
- da4ec84f28cf329c6779d008fd023df99d08fe24c9340c1bb16229d7fb0fe9a0
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