Instructions to use tlam25/phase1_bert_undersampling_appearance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tlam25/phase1_bert_undersampling_appearance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tlam25/phase1_bert_undersampling_appearance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tlam25/phase1_bert_undersampling_appearance") model = AutoModelForSequenceClassification.from_pretrained("tlam25/phase1_bert_undersampling_appearance") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e42841db70c2f9c4ef1a08df99513fe353045f903f1bca94af8e7b3d9f8627be
- Size of remote file:
- 438 MB
- SHA256:
- 4341abf837d89a20eddf06a6d6dce31de2b82b01a3cfd293cbbfb88f8f28af10
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