Fine Tuning A sentence transformer model with my own data
Hi all!Cheers to this big community (and my first post here)I am trying to fine tune a sentence transformer model. The data I have contains below columns:raw_text - the raw chunks of textlabel - corresponding label for the text - True or False.I wanted to fine tune a sentence transformer model such that the embeddings are optimized in a way that  all the True sentences are closer in the vector space than all the False sentence.I have been reading about the losses fromLoss Overview — Sentence-Transformers documentationI am really confused which loss to use for my type of data and use-case. I am leaned towards below:image1722×262 22.9 KBsince it matches my data format. As I read more about these losses and the way they are being computed using anchor, positive and negative samples I feel less confident in using them since my data does not have these kind of pair.Can someone here help me understand if what I am trying to do is plausible with existing losses in sentence transformer library?
2024-03-12T19:51:20Z
Hi,Refer to this blog post:Train and Fine-Tune Sentence Transformers Models. The blog post has asectionon various cases that your dataset may be in, and which loss functions one can use correspondingly.
2024-04-17T17:31:12Z
I am trying to train the Sentence Transformer model also checked the link but always I get the accuracy of only 20 to 30%.Is it maybe base model ? what base model to trainHow to evaluate these models?