Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/bert-base-cased") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/bert-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a7ac618d34bff9986b28faca836fc80076755e4d4e379332f7445f08793a3d2
- Size of remote file:
- 4.92 kB
- SHA256:
- 15be8b8527640eed95000ae3add7b2970781d1e199e7d110cd9f2593b62d1de0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.