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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 788f23404acfaae607e6ca1dfedaa5427fbad81e3fd5d94241a37723cdae29af
- Size of remote file:
- 4.92 kB
- SHA256:
- 9a9e884913346487b64f991638d8232fae6de699662a1a7533a69bd91814343f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.