Instructions to use kwang123/bert-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kwang123/bert-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kwang123/bert-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kwang123/bert-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("kwang123/bert-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kwang123/bert-sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/kwang123/bert-sentiment-analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kwang123/bert-sentiment-analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kwang123/bert-sentiment-analysis/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ea5e5e1a37454492d2de28566f04c95000424dd82d1589417388ca63ad9a9a6e
- Size of remote file:
- 438 MB
- SHA256:
- b84ab3e674ff35f1202b7e615f38475e57371cb09b68ecf6244a7388db32b263
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.