Update README.md
Browse files
README.md
CHANGED
@@ -1524,7 +1524,7 @@ license: apache-2.0
|
|
1524 |
---
|
1525 |
|
1526 |
# [bilingual-embedding-base](https://huggingface.co/Lajavaness/bilingual-embedding-base)
|
1527 |
-
This repo is a fork of the original [Lajavaness/bilingual-embedding-base](https://huggingface.co/Lajavaness/bilingual-embedding-base)
|
1528 |
|
1529 |
Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
|
1530 |
|
|
|
1524 |
---
|
1525 |
|
1526 |
# [bilingual-embedding-base](https://huggingface.co/Lajavaness/bilingual-embedding-base)
|
1527 |
+
This repo is a fork of the original [Lajavaness/bilingual-embedding-base](https://huggingface.co/Lajavaness/bilingual-embedding-base). The only difference is the model type name, to be compatible with text-embeddings-inference.
|
1528 |
|
1529 |
Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
|
1530 |
|