Feature Extraction
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use hotchpotch/mMiniLMv2-L12-H384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hotchpotch/mMiniLMv2-L12-H384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hotchpotch/mMiniLMv2-L12-H384")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hotchpotch/mMiniLMv2-L12-H384") model = AutoModel.from_pretrained("hotchpotch/mMiniLMv2-L12-H384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7a5a928e110ed29a0578f5c0628abf3aac5f5c41da6754a0b37118e35fc1448
- Size of remote file:
- 4.66 kB
- SHA256:
- fabeb0d55428afbaecf961720587a8fc3fcec446016340fb96b11f56860b1255
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