Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Tingli/bert-base-banking77-pt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tingli/bert-base-banking77-pt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tingli/bert-base-banking77-pt2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tingli/bert-base-banking77-pt2") model = AutoModelForSequenceClassification.from_pretrained("Tingli/bert-base-banking77-pt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tingli/bert-base-banking77-pt2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Tingli/bert-base-banking77-pt2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tingli/bert-base-banking77-pt2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tingli/bert-base-banking77-pt2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 242da319808e52fc58d47feb4b0f69df8ddb9a7f054bc42ee023bc56741c2227
- Size of remote file:
- 438 MB
- SHA256:
- fb93f2d95cf09c5e40f82fdd48aeb64ba37dc2df809407074f2b5a5b23986ba9
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