Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use LogischeIP/SentimentT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LogischeIP/SentimentT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LogischeIP/SentimentT2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LogischeIP/SentimentT2") model = AutoModelForSequenceClassification.from_pretrained("LogischeIP/SentimentT2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cab8bd496ff200d115e9785c7747be223a4c1bbac0ef7e3bb33f580f0c3eb00
- Size of remote file:
- 268 MB
- SHA256:
- b7baab203a18246581b440caefbbd65c339f765c6a2d781171d92a4d44f2b0ff
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