Instructions to use research-dump/distilbert-base-uncased_fold_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-dump/distilbert-base-uncased_fold_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="research-dump/distilbert-base-uncased_fold_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("research-dump/distilbert-base-uncased_fold_1") model = AutoModelForSequenceClassification.from_pretrained("research-dump/distilbert-base-uncased_fold_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6a7f658826e0e0459a0616ca0b3ff2d771485b90a2fdb05b822cd47295dbf81
- Size of remote file:
- 268 MB
- SHA256:
- 93f54d2f44052ad25fe691289e87105bcc435ce604d0142378a967d15b3b9345
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