Instructions to use JiaqiLee/imdb-finetuned-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JiaqiLee/imdb-finetuned-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JiaqiLee/imdb-finetuned-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JiaqiLee/imdb-finetuned-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("JiaqiLee/imdb-finetuned-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JiaqiLee/imdb-finetuned-bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/JiaqiLee/imdb-finetuned-bert-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JiaqiLee/imdb-finetuned-bert-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JiaqiLee/imdb-finetuned-bert-base-uncased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0d477555483a1e206870dfd99a897f591e35763f6b64ef3b79c219f0c5916ee7
- Size of remote file:
- 438 MB
- SHA256:
- 4f3c938e461b3f8e07a0dde04921828dca1b32d5175b49529806129496729268
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.