Instructions to use cnmoro/custom-model2vec-tokenlearn-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use cnmoro/custom-model2vec-tokenlearn-medium with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("cnmoro/custom-model2vec-tokenlearn-medium") - sentence-transformers
How to use cnmoro/custom-model2vec-tokenlearn-medium with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/custom-model2vec-tokenlearn-medium") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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A custom model2vec model, trained using a modified version of the [tokenlearn](https://github.com/MinishLab/tokenlearn) library.
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The output dimension is 256, and the vocabulary size is 249.999
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The training process used a mix of English (10%) and Portuguese (90%) texts.
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A custom model2vec model, trained using a modified version of the [tokenlearn](https://github.com/MinishLab/tokenlearn) library.
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Base model is nomic-ai/nomic-embed-text-v2-moe.
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The output dimension is 256, and the vocabulary size is 249.999
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The training process used a mix of English (10%) and Portuguese (90%) texts.
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