Instructions to use averageandyyy/distilbert-base-uncased-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use averageandyyy/distilbert-base-uncased-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="averageandyyy/distilbert-base-uncased-finetuned-imdb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("averageandyyy/distilbert-base-uncased-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("averageandyyy/distilbert-base-uncased-finetuned-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from averageandyyy/distilbert-base-uncased-finetuned-imdb: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/averageandyyy/distilbert-base-uncased-finetuned-imdb/resolve/main/training_args.bin
- Command line
-
hf download hf://averageandyyy/distilbert-base-uncased-finetuned-imdb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/averageandyyy/distilbert-base-uncased-finetuned-imdb/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 9ebaea5a1aab7f83e451bdd6de2c915254a84e2799556bd252c88c83ce5c9083
- Size of remote file:
- 3.64 kB
- SHA256:
- ce32b016d9473e216ac7870ae353d974080e8f62ee44f59f8296ad86730a3486
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.