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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pectics/vad-macbert") model = AutoModelForSequenceClassification.from_pretrained("Pectics/vad-macbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from Pectics/vad-macbert: direct link, hf CLI and curl.
- Browser
- Download file 92 Bytes
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https://huggingface.co/Pectics/vad-macbert/resolve/main/.gitattributes
- Command line
-
hf download hf://Pectics/vad-macbert/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/Pectics/vad-macbert/resolve/main/.gitattributes
92 Bytes
| *.safetensors filter=lfs diff=lfs merge=lfs -text | |
| *.bin filter=lfs diff=lfs merge=lfs -text | |