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If I use SoftmaxLoss in NLI example as shown in https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli.py , during training the base model Transformer+pooling is sent to https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/losses/SoftmaxLoss.py and it adds a nn.Linear layer with softmax activation on top of the base model . My evaluator is BinaryClassificationEvaluator . During evaluation instead of getting labeled output, the evaluator gets embedding for source-target sentence pairs and returns best threshold for various methods such as cosine-distance , dot similarity etc . Using that threshold for inference works fine in my case. But my question is, if there is any way to get labeled output from saved sentencetransformer(saved checkpoints during training) model .
The text was updated successfully, but these errors were encountered:
I am also trying to use softmax loss to make a two-category model, I think you can read the saved model and then get the probability of the label through the output in softmax loss.
If I use
SoftmaxLoss
in NLI example as shown in https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli.py , during training the base modelTransformer+pooling
is sent to https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/losses/SoftmaxLoss.py and it adds ann.Linear
layer with softmax activation on top of the base model . My evaluator isBinaryClassificationEvaluator
. During evaluation instead of getting labeled output, the evaluator gets embedding for source-target sentence pairs and returns best threshold for various methods such as cosine-distance , dot similarity etc . Using that threshold for inference works fine in my case. But my question is, if there is any way to get labeled output from saved sentencetransformer(saved checkpoints during training) model .The text was updated successfully, but these errors were encountered: