Instructions to use cruiser/distilbert_final_config_dropout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cruiser/distilbert_final_config_dropout with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cruiser/distilbert_final_config_dropout")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cruiser/distilbert_final_config_dropout") model = AutoModelForSequenceClassification.from_pretrained("cruiser/distilbert_final_config_dropout", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a210ead371f8f15a7126e078c1feddc2e864374060eb8619cc755b53bba3833
- Size of remote file:
- 268 MB
- SHA256:
- 3b1ba11cb5f8247a565ca729998e4b0727e7ec038a9c97297bbc891f2fa836e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.