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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
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license: mit
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datasets:
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- Helsinki-NLP/open_subtitles
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language:
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- zh
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base_model:
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- hfl/chinese-macbert-base
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pipeline_tag: text-classification
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tags:
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- agent
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