Instructions to use htet-98/mmbert_base_oversampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use htet-98/mmbert_base_oversampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="htet-98/mmbert_base_oversampling")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("htet-98/mmbert_base_oversampling") model = AutoModelForSequenceClassification.from_pretrained("htet-98/mmbert_base_oversampling") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c93bdcedf90db5e30ccf8cefc43295c77fbf2cd123f5830478c5f550fac7e9cf
- Size of remote file:
- 34.4 MB
- SHA256:
- 0a5069feab370ece9f452dcda41c0ba2c216eebf6cb49b06893698f53cdc2b0b
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