Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
Japanese
bert
Generated from Trainer
Instructions to use jarvisx17/japanese-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jarvisx17/japanese-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jarvisx17/japanese-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jarvisx17/japanese-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("jarvisx17/japanese-sentiment-analysis", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jarvisx17/japanese-sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 445 MB
-
https://huggingface.co/jarvisx17/japanese-sentiment-analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jarvisx17/japanese-sentiment-analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jarvisx17/japanese-sentiment-analysis/resolve/main/pytorch_model.bin
445 MB
- Xet hash:
- 7136c66a825c5741266139e20adc60dcc45e63bca8ac020c3b7a6c3941ffbbf3
- Size of remote file:
- 445 MB
- SHA256:
- e9c0493769c5596fa53f784bc2dde4260fcccd55ad692b021146f19d021e7e40
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.