Sentence Similarity
Transformers
Safetensors
English
Arabic
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mentee_embed
feature-extraction
embeddings
retrieval
contrastive-learning
multilingual
from-scratch
custom_code
Instructions to use MenteEAI/mentee-embed-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MenteEAI/mentee-embed-v3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MenteEAI/mentee-embed-v3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """ | |
| __init__.py — makes hf_hub a proper package and registers | |
| MenteeEmbedConfig + MenteeEmbedModel with the HuggingFace Auto classes. | |
| When HF loads a repo with trust_remote_code=True it imports this file first, | |
| which triggers the AutoModel/AutoConfig registration automatically. | |
| """ | |
| from .configuration_mentee import MenteeEmbedConfig | |
| from .modeling_mentee import MenteeEmbedModel | |
| from .tokenization_mentee import MenteeTokenizer | |
| from transformers import AutoConfig, AutoModel, AutoTokenizer | |
| AutoConfig.register("mentee_embed", MenteeEmbedConfig) | |
| AutoModel.register(MenteeEmbedConfig, MenteeEmbedModel) | |
| AutoTokenizer.register(MenteeEmbedConfig, slow_tokenizer_class=None, fast_tokenizer_class=MenteeTokenizer) | |
| __all__ = ["MenteeEmbedConfig", "MenteeEmbedModel", "MenteeTokenizer"] | |