Instructions to use mateiaassAI/MBERTbase_REDv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaassAI/MBERTbase_REDv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mateiaassAI/MBERTbase_REDv2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mateiaassAI/MBERTbase_REDv2") model = AutoModelForSequenceClassification.from_pretrained("mateiaassAI/MBERTbase_REDv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 248f38a0f2b9d04cd29a20e22c967f1db10f3a5075eaa195d90aa393411f75ca
- Size of remote file:
- 711 MB
- SHA256:
- fd3eeaf45f4101d97d014664aaebe7ea2aa8b2ac04ceb21016ba71c27e1586bf
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