Comments (3)
Hi @ankushkundaliya ,
thanks! Currently, PET does not support multi-label classification. You could try to rephrase the task as multiple single-label classifications. For example, let's say you want to know which topics from a given list (let's say, ["politics", "sports", "economics"]
) a text x
covers. You could reformulate this as three binary classification tasks, with PVPs like
P(x) = x. Is this text about politics? [MASK]
for the first taskP(x) = x. Is this text about sports? [MASK]
for the second taskP(x) = x. Is this text about economics? [MASK]
for the third task
and for all tasks, you could use a verbalizer that maps a match (i.e., the text is about the given topic) to Yes
(or True
) and a no-match to No
(or False
).
from pet.
Hi @timoschick,
I am considering using the approach you explained above to turn a multi-label classification problem (with N labels) into N binary classification tasks. However, I wonder whether there's a big difference between using a single MLM to be fine-tuned rather than different MLMs for each pattern.
Because for example suppose that you have a very large amount of labels (such as 500), fine-tuning 500 MLMs is simply not feasible. Is using a single MLM appropriate?
Kind regards,
Niels
from pet.
Hi @NielsRogge,
we've run some experiments to answer that very question (I can't go into details as the corresponding paper is currently under review), and the short answer is: Yes, you can use a single MLM. However, you'll have to modify the source code of this library accordingly, as it does not support using a single MLM out-of-the-box.
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Related Issues (20)
- Training Time Issue HOT 4
- GenPET commands with GPT-2 HOT 2
- RuntimeError on eval method HOT 2
- Token indices sequence length is longer than the specified maximum sequence length for this model HOT 2
- PET and iPET parameters
- Random seed parameter for iterations
- Data format for few-shot text classification
- Roberta-large using BPE tokenizer generates multi tokens. HOT 6
- API Usage
- How much test data do you use in your experiments?
- How to training PET model uses xlm-roberta with byte-level Byte-Pair-Encoding?
- How to reproduce results of the paper? HOT 1
- Clarification on how to interpret PET's results HOT 2
- A question about “Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification”
- RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)` HOT 3
- OSError: Model name 'clue/albert_chinese_tiny' was not found in tokenizers model name list HOT 2
- TypeError: expected str, bytes or os.PathLike object, not NoneType HOT 1
- ZeroDivisionError when reduction is set to 'wmean' while training iPET HOT 1
- Stuck at 'There are 0 Examples for Label [my_label]' During iPETs Example Selection for Next Generation
- There is no softmax
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