[docs] Update the training snippets for some losses that should use the v3 Trainer (#2987)

Author: tomaarsen

sentence_transformers/losses/Matryoshka2dLoss.py

@@ -95,21 +95,23 @@ def __init__(
         Example:
             ::
 
-                from sentence_transformers import SentenceTransformer, losses, InputExample
-                from torch.utils.data import DataLoader
+                from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
+                from datasets import Dataset
 
                 model = SentenceTransformer("microsoft/mpnet-base")
-                train_examples = [
-                    InputExample(texts=['Anchor 1', 'Positive 1']),
-                    InputExample(texts=['Anchor 2', 'Positive 2']),
-                ]
-                train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=32)
-                train_loss = losses.MultipleNegativesRankingLoss(model=model)
-                train_loss = losses.Matryoshka2dLoss(model, train_loss, [768, 512, 256, 128, 64])
-                model.fit(
-                    [(train_dataloader, train_loss)],
-                    epochs=10,
+                train_dataset = Dataset.from_dict({
+                    "anchor": ["It's nice weather outside today.", "He drove to work."],
+                    "positive": ["It's so sunny.", "He took the car to the office."],
+                })
+                loss = losses.MultipleNegativesRankingLoss(model)
+                loss = losses.Matryoshka2dLoss(model, loss, [768, 512, 256, 128, 64])
+
+                trainer = SentenceTransformerTrainer(
+                    model=model,
+                    train_dataset=train_dataset,
+                    loss=loss,
                 )
+                trainer.train()
         """
         matryoshka_loss = MatryoshkaLoss(
             model,

sentence_transformers/losses/MatryoshkaLoss.py

@@ -101,21 +101,23 @@ def __init__(
         Example:
             ::
 
-                from sentence_transformers import SentenceTransformer, losses, InputExample
-                from torch.utils.data import DataLoader
+                from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
+                from datasets import Dataset
 
                 model = SentenceTransformer("microsoft/mpnet-base")
-                train_examples = [
-                    InputExample(texts=['Anchor 1', 'Positive 1']),
-                    InputExample(texts=['Anchor 2', 'Positive 2']),
-                ]
-                train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=32)
-                train_loss = losses.MultipleNegativesRankingLoss(model=model)
-                train_loss = losses.MatryoshkaLoss(model, train_loss, [768, 512, 256, 128, 64])
-                model.fit(
-                    [(train_dataloader, train_loss)],
-                    epochs=10,
+                train_dataset = Dataset.from_dict({
+                    "anchor": ["It's nice weather outside today.", "He drove to work."],
+                    "positive": ["It's so sunny.", "He took the car to the office."],
+                })
+                loss = losses.MultipleNegativesRankingLoss(model)
+                loss = losses.MatryoshkaLoss(model, loss, [768, 512, 256, 128, 64])
+
+                trainer = SentenceTransformerTrainer(
+                    model=model,
+                    train_dataset=train_dataset,
+                    loss=loss,
                 )
+                trainer.train()
         """
         super().__init__()
         self.model = model

sentence_transformers/losses/MegaBatchMarginLoss.py

@@ -59,25 +59,30 @@ def __init__(
         Example:
             ::
 
-                from sentence_transformers import SentenceTransformer, InputExample, losses
-                from torch.utils.data import DataLoader
+                from sentence_transformers import SentenceTransformer, SentenceTransformerTrainingArguments, SentenceTransformerTrainer, losses
+                from datasets import Dataset
 
-                model = SentenceTransformer('all-MiniLM-L6-v2')
-
-                total_examples = 500
                 train_batch_size = 250
                 train_mini_batch_size = 32
 
-                train_examples = [
-                    InputExample(texts=[f"This is sentence number {i}", f"This is sentence number {i+1}"]) for i in range(total_examples)
-                ]
-                train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=train_batch_size)
-                train_loss = losses.MegaBatchMarginLoss(model=model, mini_batch_size=train_mini_batch_size)
-
-                model.fit(
-                    [(train_dataloader, train_loss)],
-                    epochs=10,
+                model = SentenceTransformer('all-MiniLM-L6-v2')
+                train_dataset = Dataset.from_dict({
+                    "anchor": [f"This is sentence number {i}" for i in range(500)],
+                    "positive": [f"This is sentence number {i}" for i in range(1, 501)],
+                })
+                loss = losses.MegaBatchMarginLoss(model=model, mini_batch_size=train_mini_batch_size)
+
+                args = SentenceTransformerTrainingArguments(
+                    output_dir="output",
+                    per_device_train_batch_size=train_batch_size,
+                )
+                trainer = SentenceTransformerTrainer(
+                    model=model,
+                    args=args,
+                    train_dataset=train_dataset,
+                    loss=loss,
                 )
+                trainer.train()
         """
         super().__init__()
         self.model = model