Comments (4)
Hi, thank you for the kind comment :)
Sorry we have not yet released the specific code for ZeroRC as it requires a different data processing pipeline to be consistent with the previous benchmark.
As I'm currently busy with another project, it may take a few weeks to integrate into the current repository.
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Thanks
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What changes to the data processing pipeline would need to be made?
Can we not just change the extraction setup to include the test head and tail entity in the prompt and get the model to generate the relation as in Figure 4b? Also is the generation of the relation conditioned on the number of unseen relations or is it over the full tokenizer vocabulary?
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I think I understand why it is more appropriate to do this in the data processing stage. Basically we need to modify all input files passed to the extractor model.
Currently it is done here in encoder.encode_to_line. We would need to make a similar function to take the head and tail entities out of the summary
and into the input text
, so the input contains the context, head and tail, and the output is just the relation.
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Related Issues (15)
- how to split single-triplet and multi-triplet HOT 1
- 有支持中文的模型吗 HOT 6
- data limits HOT 7
- Unseen Labels HOT 2
- where is the pretrain model of unseen=5 &unseen=15?
- Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation. {'target': 250, 'success': 0, 'raw': 1}
- Creating own data splits HOT 1
- how to split data for relation classification ?
- question about Algorithm 1 on step 3 and 4. HOT 1
- connection timeout error is reported when using data to generate part of the code
- Hi! I‘m trying to run this code lately. I've got a question that why there is only pretrained model for unseen=10 seed=0.WHere should I find other models? thank you HOT 6
- Demo - for line 5 and line 6 from algo HOT 1
- Zero Shot explanation HOT 2
- Bug in Data Splitting on FewRel Dataset HOT 2
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