Instructions to use hivaze/ru-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hivaze/ru-e5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hivaze/ru-e5-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hivaze/ru-e5-base") model = AutoModel.from_pretrained("hivaze/ru-e5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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This is a smaller version of the **intfloat/multilingual-e5-base** with only some Russian (Cyrillic in general) and English (fever) tokens (and embeddings) left.
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The model created in a similar way as described in this https://
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The **CulturaX** dataset was used to search for the required tokens. As a result, out of 250k tokens of the original model, only **69,382** required were left.
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This is a smaller version of the **intfloat/multilingual-e5-base** with only some Russian (Cyrillic in general) and English (fever) tokens (and embeddings) left.
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The model created in a similar way as described in this https://medium.com/m/global-identity-2?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90 post.
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The **CulturaX** dataset was used to search for the required tokens. As a result, out of 250k tokens of the original model, only **69,382** required were left.
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