Instructions to use HuggingFaceTB/finemath-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/finemath-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/finemath-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/finemath-classifier") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/finemath-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9dff1df61218cc41b8366ab02cd3abad891ce11d1960d081db8767f672ddfe5f
- Size of remote file:
- 471 MB
- SHA256:
- bad3fbea5207a34479f4068e5f44866114033fe6ee4fc86ff4d850885c49f842
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.