Text Classification
Transformers
PyTorch
TensorBoard
Bengali
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ka05ar/bert-base-multilingual-cased-VITD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ka05ar/bert-base-multilingual-cased-VITD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ka05ar/bert-base-multilingual-cased-VITD")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ka05ar/bert-base-multilingual-cased-VITD") model = AutoModelForSequenceClassification.from_pretrained("ka05ar/bert-base-multilingual-cased-VITD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 282902452701aab18a0451ffdc6c1b82aa427a675b5ba16d5ec50ac8e7ed05fe
- Size of remote file:
- 3.96 kB
- SHA256:
- 6e0b8a127bc0b321880ad238c35c666b5993e5388f4cfe5e1d7f1d79d9527f5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.