OpenGVLab / OpenGVLab/InternVideo
About the text features used in Grounding task.
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Description
Dear team,
Thanks for your great job.
I would like to know how to get the text feature for the grounding task.
I see that you utilize the LLAMA backbone with chinese_alpaca_lora_7b, however, I see a mismatch for the token dim.
The number of tokens for the tokenized original sentence and the number of tokens' dim in the text feature you extracted is always smaller by 5, which is a consistent number.
I want to know, except for the global token, are there any new tokens added in the sentences?
Thank you!
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Research direction
The issue names no files, tests, or entry points. Start by locating the grounding-task text-feature extraction and LLAMA tokenization paths, then compare the tokenized sentence length with the extracted feature dimensions. Done means documenting which tokens account for the consistent difference and how to obtain the grounding text features.
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Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100