XLMRoberta embeddings not differentiating between different sentences
robfuller7 opened this issue · 1 comments
Is there an existing issue for this?
- I have searched the existing issues and did not find a match.
Who can help?
No response
What are you working on?
As part of a dissertation, I am attempting to use cosine similarity to compare embeddings in order to assess the quality of machine translated text. Specifically I want to compare pairs of Russian original and English translated sentences. I am trialling various models for this including XLMRoberta. I am running this in Google Colab.
Current Behavior
The difference in cosine similarity between a pair of sentences with a validated, good quality translation and a translation which has no relation to the original at all is currently negligible.
Expected Behavior
Cosine similarity for a good translation is high and low for a poor one.
Steps To Reproduce
https://colab.research.google.com/drive/10rtJmAkDgZLp5sVHsmeqMPZpKQ4NUkfu?usp=drive_link
Spark NLP version and Apache Spark
See notebook
Type of Spark Application
Python Application
Java Version
No response
Java Home Directory
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Setup and installation
See notebook
Operating System and Version
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Link to your project (if available)
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Additional Information
No response