Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use chaudharyakhil/bert-base-banking77-pt2-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chaudharyakhil/bert-base-banking77-pt2-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chaudharyakhil/bert-base-banking77-pt2-testing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chaudharyakhil/bert-base-banking77-pt2-testing") model = AutoModelForSequenceClassification.from_pretrained("chaudharyakhil/bert-base-banking77-pt2-testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d734e460ec48af9a040c9ca6ad60d14c9d81ac6f0e54199936e712ef10093d63
- Size of remote file:
- 5.18 kB
- SHA256:
- b32dd87bc1b6b782627dffc8993ad6b2d930434cc85d440067731a5dec236c1e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.