Text Classification
Transformers
TensorBoard
Safetensors
bert
Lee
10_class
Generated from Trainer
text-embeddings-inference
Instructions to use boramna/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boramna/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boramna/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boramna/model_output") model = AutoModelForSequenceClassification.from_pretrained("boramna/model_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c03eb3bf6616d8c6fe13eacc03553239cfdbea0131badb275b85d6b5191d4771
- Size of remote file:
- 5.37 kB
- SHA256:
- 1dedc797ff515d5a5a9f8f790fb519b85ce45f86217a662d49395f76411c3a91
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.