Instructions to use SchuylerH/bert-multilingual-go-emtions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SchuylerH/bert-multilingual-go-emtions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SchuylerH/bert-multilingual-go-emtions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SchuylerH/bert-multilingual-go-emtions") model = AutoModelForSequenceClassification.from_pretrained("SchuylerH/bert-multilingual-go-emtions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c30955d25060e54c7510c32d9cfee6fb4453b83ade35bed423711adfea76e36
- Size of remote file:
- 712 MB
- SHA256:
- 8f59d55908a8e474542135693c37c7186fb0f5402f499242229f4538aee582f6
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