update batch hard example

examples/training/multilingual/make_multilingual.py

@@ -77,8 +77,8 @@ def download_corpora(filepaths):
 
 
 # Here we define train train and dev corpora
-train_corpus = "../datasets/ted2020.tsv.gz"         # Transcripts of TED talks, crawled 2020
-sts_corpus = "../datasets/STS2017-extended.zip"     # Extended STS2017 dataset for more languages
+train_corpus = "datasets/ted2020.tsv.gz"         # Transcripts of TED talks, crawled 2020
+sts_corpus = "datasets/STS2017-extended.zip"     # Extended STS2017 dataset for more languages
 parallel_sentences_folder = "parallel-sentences/"
 
 # Check if the file exists. If not, they are downloaded

examples/training/other/training_batch_hard_trec_continue_training.py → examples/training/other/training_batch_hard_trec.py

@@ -9,21 +9,15 @@
 too easy and the network fails to learn good representations.
 
 Batch hard triplet loss (https://arxiv.org/abs/1703.07737) creates triplets on the fly. It requires that the
-data is labeled (e.g. labels A, B, C) and we assume that samples with the same label are similar:
-A sent1; A sent2; B sent3; B sent4
-...
+data is labeled (e.g. labels 1, 2, 3) and we assume that samples with the same label are similar:
 
-In a batch, it checks for sent1 with label A what is the other sentence with label A that is the furthest (hard positive)
+In a batch, it checks for sent1 with label 1 what is the other sentence with label 1 that is the furthest (hard positive)
 which sentence with another label is the closest (hard negative example). It then tries to optimize this, i.e.
 all sentences with the same label should be close and sentences for different labels should be clearly seperated.
 """
 
-from sentence_transformers import (
-    SentenceTransformer,
-    SentenceLabelDataset,
-    LoggingHandler,
-    losses,
-)
+from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util
+from sentence_transformers.datasets import SentenceLabelDataset
 from torch.utils.data import DataLoader
 from sentence_transformers.readers import InputExample
 from sentence_transformers.evaluation import TripletEvaluator
@@ -32,38 +26,41 @@
 
 import logging
 import os
-import urllib.request
 import random
 from collections import defaultdict
 
+logging.basicConfig(
+    format="%(asctime)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=logging.INFO,
+    handlers=[LoggingHandler()],
+)
+
 # Inspired from torchnlp
 def trec_dataset(
     directory="datasets/trec/",
     train_filename="train_5500.label",
     test_filename="TREC_10.label",
     validation_dataset_nb=500,
     urls=[
-        "http://cogcomp.org/Data/QA/QC/train_5500.label",
-        "http://cogcomp.org/Data/QA/QC/TREC_10.label",
+        "https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label",
+        "https://cogcomp.seas.upenn.edu/Data/QA/QC/TREC_10.label",
     ],
 ):
-
     os.makedirs(directory, exist_ok=True)
 
     ret = []
     for url, filename in zip(urls, [train_filename, test_filename]):
         full_path = os.path.join(directory, filename)
-        urllib.request.urlretrieve(url, filename=full_path)
+        if not os.path.exists(full_path):
+            util.http_get(url, full_path)
 
         examples = []
         label_map = {}
         guid=1
         for line in open(full_path, "rb"):
             # there is one non-ASCII byte: sisterBADBYTEcity; replaced with space
             label, _, text = line.replace(b"\xf0", b" ").strip().decode().partition(" ")
-
-            # We extract the upper category (e.g. DESC from DESC:def)
-            label, _, _ = label.partition(":")
 
             if label not in label_map:
                 label_map[label] = len(label_map)
@@ -117,12 +114,6 @@ def triplets_from_labeled_dataset(input_examples):
     return triplets
 
 
-logging.basicConfig(
-    format="%(asctime)s - %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=logging.INFO,
-    handlers=[LoggingHandler()],
-)
 
 # You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base
 model_name = 'paraphrase-distilroberta-base-v1'
@@ -140,19 +131,16 @@ def triplets_from_labeled_dataset(input_examples):
 logging.info("Loading TREC dataset")
 train_set, dev_set, test_set = trec_dataset()
 
+# We create a special dataset "SentenceLabelDataset" to wrap out train_set
+# It will yield batches that contain at least two samples with the same label
+train_data_sampler = SentenceLabelDataset(train_set)
+train_dataloader = DataLoader(train_data_sampler, batch_size=32, drop_last=True)
 
 
 # Load pretrained model
+logging.info("Load model")
 model = SentenceTransformer(model_name)
 
-logging.info("Read TREC train dataset")
-train_dataset = SentenceLabelDataset(
-    examples=train_set,
-    model=model,
-    provide_positive=False, #For BatchHardTripletLoss, we must set provide_positive and provide_negative to False
-    provide_negative=False,
-)
-train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
 
 ### Triplet losses ####################
 ### There are 4 triplet loss variants:
@@ -169,14 +157,12 @@ def triplets_from_labeled_dataset(input_examples):
 
 
 logging.info("Read TREC val dataset")
-dev_evaluator = TripletEvaluator.from_input_examples(dev_set, name='dev')
+dev_evaluator = TripletEvaluator.from_input_examples(dev_set, name='trec-dev')
 
 logging.info("Performance before fine-tuning:")
 dev_evaluator(model)
 
-warmup_steps = int(
-    len(train_dataset) * num_epochs / train_batch_size * 0.1
-)  # 10% of train data
+warmup_steps = int(len(train_dataloader) * num_epochs  * 0.1)  # 10% of train data
 
 # Train the model
 model.fit(
@@ -195,5 +181,5 @@ def triplets_from_labeled_dataset(input_examples):
 ##############################################################################
 
 logging.info("Evaluating model on test set")
-test_evaluator = TripletEvaluator.from_input_examples(test_set, name='test')
+test_evaluator = TripletEvaluator.from_input_examples(test_set, name='trec-test')
 model.evaluate(test_evaluator)

sentence_transformers/__init__.py

@@ -1,6 +1,6 @@
 __version__ = "0.4.0"
 __DOWNLOAD_SERVER__ = 'https://sbert.net/models/'
-from .datasets import SentencesDataset, SentenceLabelDataset, ParallelSentencesDataset
+from .datasets import SentencesDataset, ParallelSentencesDataset
 from .LoggingHandler import LoggingHandler
 from .SentenceTransformer import SentenceTransformer
 from .readers import InputExample