Adding the ListConRanker model discussed in #2584

fixes #2584

Since ListConRanker is a model with a new architecture, it does not support direct loading using the transformers library. We recommend cloning the open-source model and then loading it locally. We have shown an example below and at the top of the code.

import mteb
import sys
model = mteb.get_model('ByteDance/ListConRanker', listconranker_local_path=YOUR_LOCAL_MODEL_PATH)
tasks = mteb.get_tasks(tasks=['T2Reranking], languages=['zho-Hans'])
evaluation = mteb.MTEB(tasks=tasks)
encode_kwargs = {'batch_size': sys.maxsize}
evaluation.run(model, encode_kwargs=encode_kwargs, previous_results=YOUR_LOCAL_PREVIOUS_RESULTS_PATH)

Checklist

• I did not add a dataset, or if I did, I added the dataset checklist to the PR and completed it.
• I did not add a model, or if I did, I added the model checklist to the PR and completed it.
  • I have filled out the ModelMeta object to the extent possible
  • I have ensured that my model can be loaded using
  • mteb.get_model(model_name, revision) and
  • mteb.get_model_meta(model_name, revision)
  • I have tested the implementation works on a representative set of tasks.
  • The model is public, i.e. is available either as an API or the wieght are publicly avaiable to download