Feature Extraction
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use hotchpotch/mMiniLMv2-L6-H384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hotchpotch/mMiniLMv2-L6-H384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hotchpotch/mMiniLMv2-L6-H384")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hotchpotch/mMiniLMv2-L6-H384") model = AutoModel.from_pretrained("hotchpotch/mMiniLMv2-L6-H384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6f245d5141ef77b3b89d7b7b9abc5ffcc095aad2908a46268b077c87d7319b6
- Size of remote file:
- 17.1 MB
- SHA256:
- 69564b696052886ed0ac63fa393e928384e0f8caada38c1f4864a9bfbf379c15
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