Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use GioReg/bertMULTINEGsentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GioReg/bertMULTINEGsentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GioReg/bertMULTINEGsentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GioReg/bertMULTINEGsentiment") model = AutoModelForSequenceClassification.from_pretrained("GioReg/bertMULTINEGsentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GioReg/bertMULTINEGsentiment: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/GioReg/bertMULTINEGsentiment/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GioReg/bertMULTINEGsentiment/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GioReg/bertMULTINEGsentiment/resolve/main/pytorch_model.bin
670 MB
- Xet hash:
- 0010e17defa74eeb78dd2c9b31aa2fc95728a1a2787cc34900089d2fe9c57f81
- Size of remote file:
- 670 MB
- SHA256:
- 784751a5ea7321f55a060c6a5db34af5ebd2de49d316a90703deb4ad6143ee79
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