Instructions to use mr4/bert-base-jp-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mr4/bert-base-jp-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mr4/bert-base-jp-sentiment-analysis", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mr4/bert-base-jp-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("mr4/bert-base-jp-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d702ec902905582a0afcb7bd74ba993cf8042a3d8ff72f3bc2750eaad1f02963
- Size of remote file:
- 445 MB
- SHA256:
- 5837097dd474ddfd4718193c1d9c3df1ad5b7ab33cf0ec92ab033195fe39f18b
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