Instructions to use tlam25/phase1_bert_upsampling_replace_appearance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tlam25/phase1_bert_upsampling_replace_appearance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tlam25/phase1_bert_upsampling_replace_appearance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tlam25/phase1_bert_upsampling_replace_appearance") model = AutoModelForSequenceClassification.from_pretrained("tlam25/phase1_bert_upsampling_replace_appearance") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d83ad3373468dc898892cff292e25845fe5742816f7e67e4eaf1c247dbfe6364
- Size of remote file:
- 438 MB
- SHA256:
- a849b6aa19bf7a9e948534a20f266536f9e7a9ea8bfb03b67103fc49b050ae99
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