Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/bert-base-banking77-pt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/bert-base-banking77-pt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/bert-base-banking77-pt2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/bert-base-banking77-pt2") model = AutoModelForSequenceClassification.from_pretrained("philschmid/bert-base-banking77-pt2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb5ec07fca88da39ae161743d754b25ec71fa640b76ee2a2e99effafc62e659c
- Size of remote file:
- 438 MB
- SHA256:
- cd61670e800cdf74ff8fc5c87006f08014ea896a1325ac6be031dc2354faed0c
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