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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ka05ar/bert-base-multilingual-cased-VITD: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/ka05ar/bert-base-multilingual-cased-VITD/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ka05ar/bert-base-multilingual-cased-VITD/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ka05ar/bert-base-multilingual-cased-VITD/resolve/main/pytorch_model.bin
711 MB
- Xet hash:
- 21e771452918629fc4665db121c9d0597ecb56e2403560a603bc463b70f79b77
- Size of remote file:
- 711 MB
- SHA256:
- 89a79af81a4fcc5dec67f9e98c1ecf2843a3158079b8006bf230f0300c3beee7
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