Instructions to use erickdp/mMiniLM-1215 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erickdp/mMiniLM-1215 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="erickdp/mMiniLM-1215")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("erickdp/mMiniLM-1215") model = AutoModelForSequenceClassification.from_pretrained("erickdp/mMiniLM-1215", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 212bfaaf7beffc70a3f2b8fbd9929f72e086ac0287cc39a5d98486b405c9209a
- Size of remote file:
- 17.1 MB
- SHA256:
- 46afe88da5fd71bdbab5cfab5e84c1adce59c246ea5f9341bbecef061891d0a7
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