Instructions to use research-dump/bert_base_temp_classifier_boot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-dump/bert_base_temp_classifier_boot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="research-dump/bert_base_temp_classifier_boot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("research-dump/bert_base_temp_classifier_boot") model = AutoModelForSequenceClassification.from_pretrained("research-dump/bert_base_temp_classifier_boot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8601ce1d2adab1b98e36d457f85340ec7b3d9753d2c78b4b169317fe6e43e0e5
- Size of remote file:
- 433 MB
- SHA256:
- a3b0ba37f3dea871023b87fff6f6e6de7388363210628eef8b1593a996d78f6c
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