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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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This is a ColBERT model finetuned from google/bert_uncased_L-2_H-128_A-2 on the msmarco-bm25 dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. This model is primarily designed for unit tests in limited compute environments such as GitHub Actions. But it does work to an extent for basic use cases. As of Sentence Transformers v6.0.0, this model loads directly as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

Embeddings & similarity
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This model is an export of these GloVe-6B English Vectors (_300d_) for `staticvectors`. `staticvectors` enables running inference in Python with NumPy. This helps it maintain solid runtime performance. _This model is a quantized version of the base model. It's using 10x256 Product Quantization._

Embeddings & similarity
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This model is an export of this FastText Language Identification model for `staticvectors`. `staticvectors` enables running inference in Python with NumPy. This helps it maintain solid runtime performance. Language detection is an important task and identification with n-gram models is an efficient and highly accurate way to do it. _This model is a quantized version of the base language id model. It's using 2x256 Product Quantization like the original quantized model from FastText. This shrinks this model down to 4MB with only a minor hit on accuracy._

Text analysis
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This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with ttstokenizer. Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated with this method.

Speech synthesis
Input Text→ Output Audio
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This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title pairs. PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance. This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).

Text analysisOpen weights
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This is a PyLate model finetuned from google/bert_uncased_L-2_H-128_A-2 on the msmarco-bm25 dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. This model is primarily designed for unit tests in limited compute environments such as GitHub Actions. But it does work to an extent for basic use cases.

Embeddings & similarity
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