Instructions to use dlb/MentalBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dlb/MentalBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dlb/MentalBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dlb/MentalBERTa") model = AutoModelForMaskedLM.from_pretrained("dlb/MentalBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb20671713d1aae268f2733bf90e3acd4fe7b7a4a192785b7cb3803f67a28efc
- Size of remote file:
- 1.42 GB
- SHA256:
- 9cd0c8288b56805cc9da1730de40ff406c8d03e3a4a863bd0a0c28fe2f60776a
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