Setu4993
Rank:Average Model Cost: $0.0000
Number of Runs: 37,511
Models by this creator
LEALLA-small
LEALLA-small
LEALLA-small Model description LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from LaBSE. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval. Model: HuggingFace's model hub. Paper: arXiv. Original model: TensorFlow Hub. Conversion from TensorFlow to PyTorch: GitHub. This is migrated from the v1 model on the TF Hub. The embeddings produced by both the versions of the model are equivalent. Though, for some of the languages (like Japanese), the LEALLA models appear to require higher tolerances when comparing embeddings and similarities. Usage Using the model: To get the sentence embeddings, use the pooler output: Output for other languages: For similarity between sentences, an L2-norm is recommended before calculating the similarity: Details Details about data, training, evaluation and performance metrics are available in the original paper. BibTeX entry and citation info
$-/run
184
Huggingface
LEALLA-large
LEALLA-large
LEALLA-large Model description LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from LaBSE. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval. Model: HuggingFace's model hub. Paper: arXiv. Original model: TensorFlow Hub. Conversion from TensorFlow to PyTorch: GitHub. This is migrated from the v1 model on the TF Hub. The embeddings produced by both the versions of the model are equivalent. Though, for some of the languages (like Japanese), the LEALLA models appear to require higher tolerances when comparing embeddings and similarities. Usage Using the model: To get the sentence embeddings, use the pooler output: Output for other languages: For similarity between sentences, an L2-norm is recommended before calculating the similarity: Details Details about data, training, evaluation and performance metrics are available in the original paper. BibTeX entry and citation info
$-/run
102
Huggingface
LEALLA-base
LEALLA-base
LEALLA-base Model description LEALLA is a collection of lightweight language-agnostic sentence embedding models supporting 109 languages, distilled from LaBSE. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval. Model: HuggingFace's model hub. Paper: arXiv. Original model: TensorFlow Hub. Conversion from TensorFlow to PyTorch: GitHub. This is migrated from the v1 model on the TF Hub. The embeddings produced by both the versions of the model are equivalent. Though, for some of the languages (like Japanese), the LEALLA models appear to require higher tolerances when comparing embeddings and similarities. Usage Using the model: To get the sentence embeddings, use the pooler output: Output for other languages: For similarity between sentences, an L2-norm is recommended before calculating the similarity: Details Details about data, training, evaluation and performance metrics are available in the original paper. BibTeX entry and citation info
$-/run
23
Huggingface