    InputExample(texts=['Another pair', 'Unrelated sentence'], label=0.3)]
Define your train dataset, the dataloader and the train loss
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)
train_loss = losses.CosineSimilarityLoss(model)
Tune the model
model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1, warmup_steps=100)
```
We tune the model by calling model.fit(). We pass a list of train_objectives, which constist of tuples (dataloader, loss_function). We can pass more than one tuple in order to perform multi-task learning on several datasets with different loss functions.
The fit method accepts the following parameter:
eval_rst
.. autoclass:: sentence_transformers.SentenceTransformer
    :members: fit
Evaluators
During training, we usually want to measure the performance to see if the performance improves. For this, the sentence_transformers.evaluation package exists. It contains various evaluators which we can pass to the fit-method. These evaluators are run periodically during training. Further, they return a score and only the model with the highest score will be stored on disc.
