sentence-transformers
datasets
sentence-transformers | datasets | |
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45 | 15 | |
13,793 | 18,411 | |
2.1% | 0.8% | |
9.2 | 9.5 | |
6 days ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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sentence-transformers
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External vectorization
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.
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[D] Looking for a better multilingual embedding model
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.
- Best pathway for Domain Adaptation with Sentence Transformers?
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Syntactic and Semantic surprisal using a LLM
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.
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Mean pooling in BERT
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'
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I Built an AI Search Engine that can find exact timestamps for anything on Youtube using OpenAI Whisper
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.
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Seeking advice on improving NLP search results
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.
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Dev Diary #12 - Finetune model
https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/data_augmentation (Augmented Encoding)
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[R] Customize size of Bio-BERT pre-trained embeddings
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.
- SentenceTransformer producing different sentence embedding results in Docker
datasets
- 🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
- Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples
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How to Train Large Models on Many GPUs?
https://github.com/huggingface/datasets
https://github.com/huggingface/transformers
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
https://huggingface.co/docs/datasets backed with an Arrow file or buffer
- Need help with a data science project
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Is there a text evaluation metric that does not need reference text?
I'm looking for an automatic evaluation metric that can score the first text higher (since it's more grammatically correct/better for other reasons). All the metrics for NLG I found require some reference text to match the generated text with, which I don't have.
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FauxPilot – an open-source GitHub Copilot server
And then pass that my_code.json as the dataset name.
[1] https://github.com/huggingface/datasets
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Hugging Face Introduces ‘Datasets’: A Lightweight Community Library For Natural Language Processing (NLP)
Code for https://arxiv.org/abs/2109.02846 found: https://github.com/huggingface/datasets
Quick Read | Paper | Github
- Datasets: A Community Library for Natural Language Processing
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