finetuner VS sentence-transformers

And all of that has something to do with finetuners. It basically fine-tunes AI models for specific use cases. With it can create a custom search experience that is tailored to their specific needs. I also wonder how this is going to be integrated into SEO tools soon since those tools are catered to traditional search engines.

Combining multiple lists into one is tough, but it's doable if you have the right approach. Fine-tuning GPT-3 might help, but finding enough examples is tough. You could use existing text data or manually label a set of training examples. A finetuner could be help too. It's a platform-agnostic toolkit that can fine-tune pre-trained models and it's customizable to do lots of tasks.

You can adjust the pause threshold a little longer for pauses between and phrases. You can also use the phrase detection mode, which sets a time limit for the entire phrase instead of ending the transcription prematurely. If your microphone sensitivity is low, you can also try adjusting the energy threshold. If you want, you can use finetuners.

It's possible that completion results may be identical to fine-tuned examples, but not guaranteed. Even with the same prompt, slight variations in output are expected due to the nature of probabilistic language models. You can experiment with different settings and parameters, including those with finetuners like these.

You can try creating your own dataset. Get some audio data that you want, preprocess it, and then create a custom dataset you can use to fine tune. You could use finetuners like these if you want as well.

We derived the dataset by pre-processing the deepfashion dataset using Finetuner. The image label generated by Finetuner is extracted and formatted to produce the text attribute of each product.

You can either use Hugging Face Transformers as they have a lot of pre-trained models that you can customize. Or Finetuners like this one: which is a toolkit for fine-tuning multiple models.

Very recently, a few non-English and multilingual CLIP models have appeared, using various sources of training data. In this article, we’ll evaluate a multilingual CLIP model’s performance in a language other than English, and show how you can improve it even further using Jina AI’s Finetuner.

I think you can convert your data into text and fine-tune the model on it. But that might not be the ideal way to go since you kind of base that on the model. Try transfer learning or finetuning with a finetuner.

you can try prompt-based classification or fine-tuning with a Finetuner. Prompts work well for simple tasks but fine-tuning may give better results for complex ones. Althouigh it's going to need more resources, but try both and see what works best for you.

txtai is an open-source first system. Given it's own open-source roots, like-minded projects such as sentence-transformers are prioritized during development. But that doesn't mean txtai can't work with Embeddings API services.

Ok great. My use case is not very specific, but rather general. I am looking for a model that can perform asymmetric semantic search for the languages I mentioned earlier (Urdu, Persian, Arabic etc.). I have also looked into the sentence-transformer training documentation. Do you think it would be a good idea to use the XNLI dataset for fine-tuning? Or maybe you can suggest much better dataset. Furthermore, I am not sure if fine-tuning is suitable for my task. Because my use case is general so I can use already trained model.

The task you are looking for is semantic textual similarity. There are a few models and datasets out there that can do this. I'd probably start with the SemEval2017 Task 1 task description and competition entries here and then work outward from there (using something like SemanticScholar or Papers With Code to find newer state of the art works that cite these models if needed). For what it's worth you might find that Sentence Bert (SBERT) gives good vectors for cosine similarity comparison out of the box for this task.

Check out the sentence-transformers implementation. If I don't miss anything they don't exclude CLS when the pooling strategy is set to 'mean'

Break up transcript into shorter segments and convert segments to a 768 vector array. Use a process known as embedding using our second ML model, UKP Labs BERT’s sentence transformer model.

Not sure what kind of texts you have, but these models have a max sequence limit of 512 (approx 350 words or so). If you're texts are longer than that, consider splitting them up into chunks or creating a summary and taking an embedding of that. Some clustering algorithm may be the way to go here. Here's a bunch of examples. I use agglomerative for my use case.

https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/data_augmentation (Augmented Encoding)

For vector representation you can take the mean and then pca to get the size that you want, but if you have time then use sentence transformers to train a vector representation instead.