Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use kevintf/AIA_B_HW1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevintf/AIA_B_HW1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kevintf/AIA_B_HW1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kevintf/AIA_B_HW1") model = AutoModelForSequenceClassification.from_pretrained("kevintf/AIA_B_HW1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aea95e75f22264e70feeca591e44938dca3d78e88d08b4b5ac2788ba3f8cde31
- Size of remote file:
- 4.98 kB
- SHA256:
- 2d389a261c4ad79c8031d213d930b79b34def1ca6a13f6f38b7b0de9654f7854
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.