Text Classification
Transformers
PyTorch
sentiment-head
feature-extraction
sentiment-analysis
openai-embeddings
custom_code
Instructions to use marcovise/TextEmbedding3SmallSentimentHead with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcovise/TextEmbedding3SmallSentimentHead with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcovise/TextEmbedding3SmallSentimentHead", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("marcovise/TextEmbedding3SmallSentimentHead", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56374bc0567af3cc4dd6007b7adf30415c431b884862d848d7855958894f9210
- Size of remote file:
- 1.58 MB
- SHA256:
- 570981fec53d08863b7c136f8d4c4023375634112c37f006b9cffbe3ed903bd3
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