Create training_nli_v2.py

training_nli_v2.py

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+"""
+The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
+with MultipleNegativesRankingLoss. Entailments are positive pairs and the contradiction on AllNLI dataset is added as a hard negative.
+At every 10% training steps, the model is evaluated on the STS benchmark dataset
+
+Usage:
+python training_nli_v2.py
+
+OR
+python training_nli_v2.py pretrained_transformer_model_name
+"""
+import math
+from sentence_transformers import models, losses, datasets
+from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
+from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
+import logging
+from datetime import datetime
+import sys
+import os
+import gzip
+import csv
+import random
+
+#### Just some code to print debug information to stdout
+logging.basicConfig(
+    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO, handlers=[LoggingHandler()]
+)
+#### /print debug information to stdout
+
+model_name = sys.argv[1] if len(sys.argv) > 1 else "microsoft/mpnet-base"
+train_batch_size = 2048  # The larger you select this, the better the results (usually). But it requires more GPU memory
+max_seq_length = 75
+num_epochs = 1
+
+# Save path of the model
+model_save_path = (
+    "output/training_nli_v2_" + model_name.replace("/", "-") + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+)
+
+
+# Here we define our SentenceTransformer model
+word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode="mean")
+model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+
+# Check if dataset exists. If not, download and extract  it
+nli_dataset_path = "data/AllNLI.tsv.gz"
+sts_dataset_path = "data/stsbenchmark.tsv.gz"
+
+if not os.path.exists(nli_dataset_path):
+    util.http_get("https://sbert.net/datasets/AllNLI.tsv.gz", nli_dataset_path)
+
+if not os.path.exists(sts_dataset_path):
+    util.http_get("https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path)
+
+
+# Read the AllNLI.tsv.gz file and create the training dataset
+logging.info("Read AllNLI train dataset")
+
+
+def add_to_samples(sent1, sent2, label):
+    if sent1 not in train_data:
+        train_data[sent1] = {"contradiction": set(), "entailment": set(), "neutral": set()}
+    train_data[sent1][label].add(sent2)
+
+
+train_data = {}
+with gzip.open(nli_dataset_path, "rt", encoding="utf8") as fIn:
+    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
+    for row in reader:
+        if row["split"] == "train":
+            sent1 = row["sentence1"].strip()
+            sent2 = row["sentence2"].strip()
+
+            add_to_samples(sent1, sent2, row["label"])
+            add_to_samples(sent2, sent1, row["label"])  # Also add the opposite
+
+
+train_samples = []
+for sent1, others in train_data.items():
+    if len(others["entailment"]) > 0 and len(others["contradiction"]) > 0:
+        train_samples.append(
+            InputExample(
+                texts=[sent1, random.choice(list(others["entailment"])), random.choice(list(others["contradiction"]))]
+            )
+        )
+        train_samples.append(
+            InputExample(
+                texts=[random.choice(list(others["entailment"])), sent1, random.choice(list(others["contradiction"]))]
+            )
+        )
+
+logging.info("Train samples: {}".format(len(train_samples)))
+
+
+# Special data loader that avoid duplicates within a batch
+train_dataloader = datasets.NoDuplicatesDataLoader(train_samples, batch_size=train_batch_size)
+
+
+# Our training loss
+train_loss = losses.CachedMultipleNegativesRankingLoss(model, mini_batch_size=64)
+
+
+# Read STSbenchmark dataset and use it as development set
+logging.info("Read STSbenchmark dev dataset")
+dev_samples = []
+with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn:
+    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
+    for row in reader:
+        if row["split"] == "dev":
+            score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
+            dev_samples.append(InputExample(texts=[row["sentence1"], row["sentence2"]], label=score))
+
+dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
+    dev_samples, batch_size=train_batch_size, name="sts-dev"
+)
+
+# Configure the training
+warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1)  # 10% of train data for warm-up
+logging.info("Warmup-steps: {}".format(warmup_steps))
+
+
+# Train the model
+model.fit(
+    train_objectives=[(train_dataloader, train_loss)],
+    evaluator=dev_evaluator,
+    epochs=num_epochs,
+    evaluation_steps=int(len(train_dataloader) * 0.1),
+    warmup_steps=warmup_steps,
+    output_path=model_save_path,
+    use_amp=True,
+)
+
+# Push the model to the Hugging Face hub
+model.save_to_hub("tomaarsen/mpnet-base-all-nli")
+
+##############################################################################
+#
+# Load the stored model and evaluate its performance on STS benchmark dataset
+#
+##############################################################################
+
+test_samples = []
+with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn:
+    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
+    for row in reader:
+        if row["split"] == "test":
+            score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
+            test_samples.append(InputExample(texts=[row["sentence1"], row["sentence2"]], label=score))
+
+model = SentenceTransformer(model_save_path)
+test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
+    test_samples, batch_size=train_batch_size, name="sts-test"
+)
+test_evaluator(model, output_path=model_save_path)