Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use asparius/bert-base-combined-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asparius/bert-base-combined-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asparius/bert-base-combined-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("asparius/bert-base-combined-large") model = AutoModelForSequenceClassification.from_pretrained("asparius/bert-base-combined-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 538878e8f5cc47e44a69bca44de4c7b1aa4bfa7089c8547a64bf69557cd9b8e0
- Size of remote file:
- 3.38 kB
- SHA256:
- 159d65c27ccedf9a32f500631d68cc8c5df96e9036c007e91d086ef255ad4f90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.