sentence-transformers were recently added in the model hub. I wanted to know the following if :
- all these models were trained on NLI (which dataset ? MNLI)
- for how many epochs ?
- If I want to use it for inference on NLI task, do I need to train an additional linear layer ? So freeze the
AutoModel, train a
nn.Linear for 1 epoch on the
mean output and then perform inference. Can you please confirm this usage ?
tfmr = AutoModel.from_pretrained("sentence-transformers/bert-base-nli-mean-tokens")
def __init__(self, model):
self.encoder = model
for param in self.encoder.parameters():
param.requires_grad = False
self.classifier = nn.Linear(768, 3)
self.criterion = nn.CrossEntropy()
def forward(self, inputs): # x will come from dataloader using default_data_collator
labels = inputs.pop('labels')
model_output = self.encoder(**inputs)
sentence_embeddings = mean_pooling(model_output, inputs['attention_mask'])
logits = self.classifier(sentence_embeddings)
loss = self.criterion(logits, labels)
return loss, logits
model = Model(tfmr)
I trained it on MNLI with the model frozen, it gets around 46% accuracy after epoch 1. Seems like it has not been trained on MNLI. Can anyone confirm ?
from the paper, section 3.1
We train SBERT on the combination of the SNLI
(Bowman et al., 2015) and the Multi-Genre NLI dataset.
So yes, it’s trained on MNLI .
My guess is as it’s trained using Triplet loss to produce embeddings that are semantically meaningful
and can be compared with cosine-similarity, it might nor perform well on just classification .
Thanks for the information. When I read the paper, I initially had the impression that it was trained on STS task. Given that it has been trained on both SNLI and MNLI (not sure about the percentage), I’m getting 46% accuracy when I train a MLP for one epoch. I know the objective is a bit different (not classification) but I’d expect it to do well (in frozen condition) given that it has seen both NLI datasets. Things don’t improve much even after 3 epochs.
On what objective is this model trained on ? Section 3 of the paper mentions three objective functions
triplet. As per Fig 2. they seem to be using regression for computing similarity scores at inference. As per Fig 1. it says that they used
classification objective for fine-tuning. Based on the performance I’m getting, I doubt that these weights are coming from model trained on
classification. Can someone please clarify ?
I have the same confusion, it’s not clear what was the final objective from those 3
@joeddav If you’ve some idea, can you please confirm this ?
I’m afraid I don’t have any extra insight here. I might head over to the sentence-transformers repo and see if you can find an answer or open an issue there (and then loop us back in over here once you have an answer )
I’ve opened an issue. You can follow it if interested.