Create training_nli_matryoshka.py

training_nli_matryoshka.py

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+# Matryoshka test
+from collections import defaultdict
+from typing import Dict
+import datasets
+from datasets import Dataset
+from sentence_transformers import (
+    SentenceTransformer,
+    SentenceTransformerTrainer,
+    losses,
+    evaluation,
+    TrainingArguments
+)
+from sentence_transformers.models import Transformer, Pooling
+
+def to_triplets(dataset):
+    premises = defaultdict(dict)
+    for sample in dataset:
+        premises[sample["premise"]][sample["label"]] = sample["hypothesis"]
+    queries = []
+    positives = []
+    negatives = []
+    for premise, sentences in premises.items():
+        if 0 in sentences and 2 in sentences:
+            queries.append(premise)
+            positives.append(sentences[0]) # <- entailment
+            negatives.append(sentences[2]) # <- contradiction
+    return Dataset.from_dict({
+        "anchor": queries,
+        "positive": positives,
+        "negative": negatives,
+    })
+
+snli_ds = datasets.load_dataset("snli")
+snli_ds = datasets.DatasetDict({
+    "train": to_triplets(snli_ds["train"]),
+    "validation": to_triplets(snli_ds["validation"]),
+    "test": to_triplets(snli_ds["test"]),
+})
+multi_nli_ds = datasets.load_dataset("multi_nli")
+multi_nli_ds = datasets.DatasetDict({
+    "train": to_triplets(multi_nli_ds["train"]),
+    "validation_matched": to_triplets(multi_nli_ds["validation_matched"]),
+})
+
+all_nli_ds = datasets.DatasetDict({
+    "train": datasets.concatenate_datasets([snli_ds["train"], multi_nli_ds["train"]]),
+    "validation": datasets.concatenate_datasets([snli_ds["validation"], multi_nli_ds["validation_matched"]]),
+    "test": snli_ds["test"]
+})
+
+stsb_dev = datasets.load_dataset("mteb/stsbenchmark-sts", split="validation")
+stsb_test = datasets.load_dataset("mteb/stsbenchmark-sts", split="test")
+
+training_args = TrainingArguments(
+    output_dir="checkpoints",
+    num_train_epochs=1,
+    seed=42,
+    per_device_train_batch_size=64,
+    per_device_eval_batch_size=64,
+    learning_rate=2e-5,
+    warmup_ratio=0.1,
+    bf16=True,
+    logging_steps=10,
+    evaluation_strategy="steps",
+    eval_steps=300,
+    save_steps=1000,
+    save_total_limit=2,
+    metric_for_best_model="spearman_cosine",
+    greater_is_better=True,
+)
+
+transformer = Transformer("microsoft/mpnet-base", max_seq_length=384)
+pooling = Pooling(transformer.get_word_embedding_dimension(), pooling_mode="mean")
+model = SentenceTransformer(modules=[transformer, pooling])
+
+tokenizer = model.tokenizer
+loss = losses.MultipleNegativesRankingLoss(model)
+loss = losses.MatryoshkaLoss(model, loss, [768, 512, 256, 128, 64])
+
+dev_evaluator = evaluation.EmbeddingSimilarityEvaluator(
+    stsb_dev["sentence1"],
+    stsb_dev["sentence2"],
+    [score / 5 for score in stsb_dev["score"]],
+    main_similarity=evaluation.SimilarityFunction.COSINE,
+    name="sts-dev",
+)
+
+trainer = SentenceTransformerTrainer(
+    model=model,
+    evaluator=dev_evaluator,
+    args=training_args,
+    train_dataset=all_nli_ds["train"],
+    # eval_dataset=all_nli_ds["validation"],
+    loss=loss,
+)
+trainer.train()
+
+test_evaluator = evaluation.EmbeddingSimilarityEvaluator(
+    stsb_test["sentence1"],
+    stsb_test["sentence2"],
+    [score / 5 for score in stsb_test["score"]],
+    main_similarity=evaluation.SimilarityFunction.COSINE,
+    name="sts-test",
+)
+results = test_evaluator(model)
+print(results)