readme: finetuning (#43)

- finetuning details (562d3a6d7afdcf505718163e9503561f060a40d8)
- minor change (d2adb21c39ae110976287aeecf030a06fe2a0658)
- simplify setting a task (bd2d9c1701c033fdbd661c40ebaa6156d3c6757b)

README.md

@@ -25178,6 +25178,25 @@ embeddings = model.encode(
 )
 ```
 
+You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html). 
+To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization:
+```python
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'})
+```
+Or afterwards:
+```python
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
+model[0].default_task = 'classification'
+```
+This way you can fine-tune the LoRA adapter for the chosen task.
+
+However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model:
+```python
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True})
+```
+This will allow fine-tuning the whole model instead of just the LoRA adapters.
+
+
 **<details><summary>ONNX Inference.</summary>**
 <p>