Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ndiy/ASPECT_SENT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndiy/ASPECT_SENT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndiy/ASPECT_SENT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ndiy/ASPECT_SENT") model = AutoModelForSequenceClassification.from_pretrained("ndiy/ASPECT_SENT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb4ced441f11435f3d2a255f01b135299c8e401b124ccc6fda148194d3411588
- Size of remote file:
- 409 MB
- SHA256:
- 977260f8172675cb2f205c7bce6c10e82b7c8da77804dbf257cf22416d6ee5d2
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