Hey, we assume that mention detection was already done and SapBERT only handles the entity normalisation part. If you'd like to input a full sentence then first another entity detection is needed to identify the entities.

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Thanks for the fast and concise answer! I already thought it could be like that.

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Just a follow up question: What mention-detection tool would you recommend?

I found that sciSpacy reports F1 as follows:

I was surprised these scores are so low, so I tested two sentences:

1. "The Calvin–Benson–Bassham (CBB) cycle is presumably evolved for optimal synthesis of C3 sugars, but not for the production of C2 metabolite acetyl-CoA. The carbon loss in producing acetyl-CoA from decarboxylation of C3 sugar limits the maximum carbon yield of photosynthesis."
   • it missed 1: "maximum carbon yield"
   • The bigger problem is that "Calvin-Benson-Bassham (CBB)" pathway was recognized as three different entities
2. "Here we design a synthetic malyl-CoA-glycerate (MCG) pathway to augment the CBB cycle for efficient acetyl-CoA synthesis."
   • Missed "pathway", here is the output: "(synthetic malyl-CoA-glycerate, MCG, CBB cycle, acetyl-CoA synthesis)"

Llama
Next, I considered "meta-llama/Llama-2-7b-hf", put it always produced text unrelated to the instructions.
Next I considered "meta-llama/Llama-2-7b-chat-hf" , which gave me:

1. Calvin
2. Benson
3. Bassham (CBB)
4. carbon
5. CoA
6. decarboxylation
7. C3
8. sugar
9. photosynthesis

Conclusion

So I'm still interested in the question of the first sentence: Do you have a recommenation for mention-detection ?

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