feat: first batch of results for the MTEB(Medical) benchmark

[dbuades]

As a follow up to embeddings-benchmark/mteb#1459, this PR contains the results for a list of 15 open source models in the new MTEB(Medical) benchmark.

The models included are:

• name: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
  revision: "bf3bf13ab40c3157080a7ab344c831b9ad18b5eb"

• name: "BAAI/bge-small-en-v1.5"
  revision: "5c38ec7c405ec4b44b94cc5a9bb96e735b38267a"

• name: "BAAI/bge-base-en-v1.5"
  revision: "a5beb1e3e68b9ab74eb54cfd186867f64f240e1a"

• name: "BAAI/bge-large-en-v1.5"
  revision: "d4aa6901d3a41ba39fb536a557fa166f842b0e09"

• name: "intfloat/multilingual-e5-small"
  revision: "fd1525a9fd15316a2d503bf26ab031a61d056e98"

• name: "intfloat/multilingual-e5-base"
  revision: "d13f1b27baf31030b7fd040960d60d909913633f"

• name: "intfloat/multilingual-e5-large"
  revision: "ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb"

• name: "Alibaba-NLP/gte-multilingual-base"
  revision: "7fc06782350c1a83f88b15dd4b38ef853d3b8503"

• name: "jinaai/jina-embeddings-v3"
  revision: "215a6e121fa0183376388ac6b1ae230326bfeaed"

• name: "Snowflake/snowflake-arctic-embed-m-v1.5"
  revision: "97eab2e17fcb7ccb8bb94d6e547898fa1a6a0f47"

• name: "mixedbread-ai/mxbai-embed-large-v1"
  revision: "990580e27d329c7408b3741ecff85876e128e203"

• name: "abhinand/MedEmbed-small-v0.1"
  revision: "40a5850d046cfdb56154e332b4d7099b63e8d50e"

• name: "abhinand/MedEmbed-base-v0.1"
  revision: "7a90c50263f620dff743eb9794b89a42bfc5d765"

• name: "abhinand/MedEmbed-large-v0.1"
  revision: "e621837c7904456dc37d689f97e654424de62318"

• name: "nvidia/NV-Embed-v2". # Using the code in this PR
  revision: "7604d305b621f14095a1aa23d351674c2859553a"

We also plan to add the following models once the inconsistencies are solved since we also noticed strange results for them:

• name: "Alibaba-NLP/gte-Qwen2-1.5B-instruct"
  revision: "3276994ba02b26841920728d1adcf115473c88e9"

• name: "Alibaba-NLP/gte-Qwen2-7B-instruct"
  revision: "e26182b2122f4435e8b3ebecbf363990f409b45b"

Finally, we added a bm25s baseline for the retrieval tasks, although there is an issue with clustering and reranking tasks at the moment.

My colleague @olivierr42 will take it from here since I will not be available next week.

Feel free to suggest other interesting models and we'll happily run them too 💪

[dbuades]

I haven't updated paths.json yet since when running results.py the paths.json that it is generated is very different to the one currently in main. Should we just manually add the results to the paths.json instead?

[KennethEnevoldsen]

@Samoed I see that you are reviewing the related PR (embeddings-benchmark/mteb#1436), will you have the time to take this PR as well?

[Samoed]

The results look good. If you want your model to appear on the leaderboard, you'll need to generate a paths.json. However, since a new version of the leaderboard is currently being developed, you might want to wait until it's finished