Comments (3)
You may not need to modify the transformer's BART code. Just look at what we have done for T5, we wrap the original forward function of each transformer layer with a new forward function which passes past_key_values
to the original transformer forward function. What need to be done to BART will be similar (I think). However, since we haven't support bart model yet, this functionality may be postponed to one or two weeks later (After the ACL :)
from openprompt.
PRs are welcomed!
from openprompt.
PRs are welcomed!
Hello author,have you ever used gpt2 for prompt test?If I want to output mask after the input without textual content,it will show "IndexError:index is out of bounds for dimension with size 0". Did I write something wrong?
For example:
promptTemplate = ManualTemplate( text = '{"placeholder":"text_a"} {"mask"}' , tokenizer = tokenizer )
promptVerbalizer = ManualVerbalizer( classes = classes, label_words = { "1": ["one"], "2": ["two"], "3": ["three"] }, tokenizer = tokenizer )
from openprompt.
Related Issues (20)
- about outputs features HOT 1
- Does openprompt support bart model?
- Not compatible with huggingface dataset
- NotImplementedError: parallelize method was not implemented for this plm.
- openprompt的SoftTemplate是否支持deberta模型
- use transformers Trainer when training
- Does OpenPrompt support LLaMa2? HOT 1
- A BUG
- 多分类使用时数据量大,能实现使用生成器模式的功能吗
- 如何加载本地模型 HOT 1
- tutorial /6.1_chinese_dataset_uer_t5.py 训练结果随机生成
- TextClassification/ dataset link is no longer available HOT 1
- tutorial data sets can not be download HOT 1
- How to solve the logits equality of LLaMA output HOT 3
- Interpret soft prompt
- Unable to pip install openprompt HOT 1
- TextClassification
- import break when using latest transformers
- OpenPrompt中文预训练模型部署
- problem with openprompt HOT 1
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from openprompt.