GIST all MiniLM L6 v2 EU AI Act Compliance Profile

GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning

The model is fine-tuned on top of the sentence-transformers/all-MiniLM-L6-v2 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task).

The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.

Technical paper: GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning

Data

The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:

• Dataset: avsolatorio/medi-data-mteb_avs_triplets
• Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb

The dataset contains a task_type key, which can be used to select only the mteb classification tasks (prefixed with mteb_).

The MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.

The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.

The retrieval performance for the TRECCOVID task is of note. The fine-tuning d

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