Instructions to use amaazallahi/bert-base-uncased-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaazallahi/bert-base-uncased-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amaazallahi/bert-base-uncased-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amaazallahi/bert-base-uncased-emotion") model = AutoModelForSequenceClassification.from_pretrained("amaazallahi/bert-base-uncased-emotion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93ed5ffc4666d203fad748e7936877021caa11a05d2bff0f30b28930d994efb3
- Size of remote file:
- 438 MB
- SHA256:
- 0990aa6b5198e1b75fba8a7f17fb71f168e04f1ad1012f8326de66827c5311d8
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