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Model: eduardofv/stsb-m-mt-es-distilbert-base-uncased
Source: Original Platform

training_stsb_m_mt.py

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+"""
+MODIFIED: (efv) Use STSb-multi-mt Spanish
+source: https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark.py
+
+---
+
+This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings
+that can be compared using cosine-similarity to measure the similarity.
+
+Usage:
+python training_nli.py
+
+OR
+python training_nli.py pretrained_transformer_model_name
+"""
+from torch.utils.data import DataLoader
+import math
+from sentence_transformers import SentenceTransformer,  LoggingHandler, losses, models, util
+from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
+from sentence_transformers.readers import InputExample
+import logging
+from datetime import datetime
+import sys
+import os
+import gzip
+import csv
+
+from datasets import load_dataset
+
+#### 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
+
+
+
+#You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base
+model_name = sys.argv[1] if len(sys.argv) > 1 else 'distilbert-base-uncased'
+
+# Read the dataset
+train_batch_size = 16
+num_epochs = 4
+model_save_path = 'output/training_stsbenchmark_'+model_name.replace("/", "-")+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+# Use Huggingface/transformers model (like BERT, RoBERTa, XLNet, XLM-R) for mapping tokens to embeddings
+word_embedding_model = models.Transformer(model_name)
+
+# Apply mean pooling to get one fixed sized sentence vector
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
+                               pooling_mode_mean_tokens=True,
+                               pooling_mode_cls_token=False,
+                               pooling_mode_max_tokens=False)
+
+model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+
+# Convert the dataset to a DataLoader ready for training
+logging.info("Read stsb-multi-mt train dataset")
+
+train_samples = []
+dev_samples = []
+test_samples = []
+
+def samples_from_dataset(dataset):
+    samples = [InputExample(texts=[e['sentence1'], e['sentence2']], label=e['similarity_score'] / 5) \
+        for e in dataset] 
+    return samples
+
+train_samples = samples_from_dataset(load_dataset("stsb_multi_mt", name="es", split="train"))
+dev_samples = samples_from_dataset(load_dataset("stsb_multi_mt", name="es", split="dev"))
+test_samples = samples_from_dataset(load_dataset("stsb_multi_mt", name="es", split="test"))
+
+train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
+train_loss = losses.CosineSimilarityLoss(model=model)
+
+
+logging.info("Read stsb-multi-mt dev dataset")
+evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
+
+
+# Configure the training. We skip evaluation in this example
+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=evaluator,
+          epochs=num_epochs,
+          evaluation_steps=1000,
+          warmup_steps=warmup_steps,
+          output_path=model_save_path)
+
+
+##############################################################################
+#
+# Load the stored model and evaluate its performance on STS benchmark dataset
+#
+##############################################################################
+
+#model = SentenceTransformer(model_save_path)
+test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='stsb-multi-mt-test')
+test_evaluator(model, output_path=model_save_path)