align readme with current mteb

README.md

@@ -50,6 +50,8 @@ model_name = "average_word_embeddings_komninos"
 # model_name = "sentence-transformers/all-MiniLM-L6-v2"
 
 model = SentenceTransformer(model_name)
+# or directly from mteb:
+model = mteb.get_model(model_name)
 tasks = mteb.get_tasks(tasks=["Banking77Classification"])
 evaluation = mteb.MTEB(tasks=tasks)
 results = evaluation.run(model, output_folder=f"results/{model_name}")
@@ -220,9 +222,13 @@ Note that the public leaderboard uses the test splits for all datasets except MS
 Models should implement the following interface, implementing an `encode` function taking as inputs a list of sentences, and returning a list of embeddings (embeddings can be `np.array`, `torch.tensor`, etc.). For inspiration, you can look at the [mteb/mtebscripts repo](https://github.com/embeddings-benchmark/mtebscripts) used for running diverse models via SLURM scripts for the paper.
 
 ```python
+import mteb
 from mteb.encoder_interface import PromptType
+from mteb.models.wrapper import Wrapper
+import numpy as np
+
 
-class CustomModel:
+class CustomModel(Wrapper):
     def encode(
         self,
         sentences: list[str],
@@ -244,7 +250,7 @@ class CustomModel:
         pass
 
 model = CustomModel()
-tasks = mteb.get_task("Banking77Classification")
+tasks = mteb.get_tasks(tasks=["Banking77Classification"])
 evaluation = MTEB(tasks=tasks)
 evaluation.run(model)
 ```