Instructions to use mk9165/mk-bert-sentiment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mk9165/mk-bert-sentiment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mk9165/mk-bert-sentiment-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mk9165/mk-bert-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("mk9165/mk-bert-sentiment-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mk9165/mk-bert-sentiment-classification: direct link, hf CLI and curl.
- Browser
- Download file 473 MB
-
https://huggingface.co/mk9165/mk-bert-sentiment-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mk9165/mk-bert-sentiment-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mk9165/mk-bert-sentiment-classification/resolve/main/pytorch_model.bin
473 MB
- Xet hash:
- 26f0725fdef1c2fadc35b296e5a1039e175af6e8cc059c90733506d997f0b1ad
- Size of remote file:
- 473 MB
- SHA256:
- 5fdcecee49b4bad66bc5014e34cd8d95937f23ca8351dde5d7309883073554d7
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