float:\n"," if epoch != -1:\n"," if steps == -1:\n"," out_txt = \" after epoch {}:\".format(epoch)\n"," else:\n"," out_txt = \" in epoch {} after {} steps:\".format(epoch, steps)\n"," else:\n"," out_txt = \":\"\n","\n"," logger.info(\"EmbeddingSimilarityEvaluator: Evaluating the model on \" + self.name + \" dataset\" + out_txt)\n","\n"," embeddings1 = model.encode(self.sentences1, batch_size=self.batch_size, show_progress_bar=self.show_progress_bar, convert_to_numpy=True)\n"," embeddings2 = model.encode(self.sentences2, batch_size=self.batch_size, show_progress_bar=self.show_progress_bar, convert_to_numpy=True)\n"," labels = self.scores\n","\n"," cosine_scores = 1 - (paired_cosine_distances(embeddings1, embeddings2))\n","\n","\n"," eval_pearson_cosine, _ = pearsonr(labels, cosine_scores)\n"," eval_spearman_cosine, _ = spearmanr(labels, cosine_scores)\n","\n"," \n","\n"," logger.info(\"Cosine-Similarity :\\tPearson: {:.4f}\\tSpearman: {:.4f}\".format(\n"," eval_pearson_cosine, eval_spearman_cosine))\n"," \n"," if output_path is not None and self.write_csv:\n"," csv_path = os.path.join(output_path, self.csv_file)\n"," output_file_exists = os.path.isfile(csv_path)\n"," with open(csv_path, newline='', mode=\"a\" if output_file_exists else 'w', encoding=\"utf-8\") as f:\n"," writer = csv.writer(f)\n"," if not output_file_exists:\n"," writer.writerow(self.csv_headers)\n","\n"," writer.writerow([epoch, steps, eval_pearson_cosine, eval_spearman_cosine])\n","\n"," return eval_spearman_cosine"],"metadata":{"id":"CGqX6HWuMgV2","executionInfo":{"status":"ok","timestamp":1659320280423,"user_tz":180,"elapsed":426,"user":{"displayName":"Ennio Ferreira","userId":"13783596981270154914"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["#################################################################################################\n","# https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/data_augmentation/train_sts_indomain_bm25.py\n","# Step 3: Train bi-encoder model with both (gold + silver) STSbenchmark dataset - Augmented SBERT\n","#\n","#################################################################################################\n","\n","import logging\n","import math\n","import random\n","import joblib\n","import math\n","from sentence_transformers import models, SentenceTransformer, InputExample, losses, InputExample,LoggingHandler\n","import logging\n","from torch.utils.data import DataLoader\n","#from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n","\n","silver_samples = []\n","for s in tqdm(sentences):\n"," silver_samples.append(InputExample(texts=(s[\"sentence_1\"],s[\"sentence_2\"]), label=s[\"cross\"]))\n","gold_eval_samples = joblib.load(os.path.join(folder_path,'gold_train_dataloader.pkl'))\n","\n","random.seed(2022)\n","samples = silver_samples\n","random.shuffle(samples)\n","train_sample = samples[:int(len(samples) * 0.99)]\n","test_sample = samples[int(len(samples) * 0.99):]\n","#random.shuffle(test_sample)"],"metadata":{"id":"3rY-kvqdJPoK","executionInfo":{"status":"ok","timestamp":1659320186732,"user_tz":180,"elapsed":5741,"user":{"displayName":"Ennio Ferreira","userId":"13783596981270154914"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"803c4608-8df2-435f-bb1c-26d0bba2815a"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 652967/652967 [00:02<00:00, 222652.04it/s]\n"]}]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5432,"status":"ok","timestamp":1659320199320,"user":{"displayName":"Ennio Ferreira","userId":"13783596981270154914"},"user_tz":180},"id":"ZiuubOiNpX_T","outputId":"b4204495-6da1-4f77-c5a0-4900332ef881"},"outputs":[{"output_type":"stream","name":"stdout","text":["copied run.ipynb\n","copied gold_eval_dataloader.pkl\n","copied gold_sample_index.txt\n","copied gold_train_dataloader.pkl\n","copied request_solr.py\n","copied train_biencoder.py\n","copied silver_data.pkl\n","copied 2_train_sts_cross_bm25.py\n","copied cross_silver_scores_v2.pkl\n","copied silver_cross_samples.pkl\n","copied cross_silver_scores_v3.pkl\n"]}],"source":["source_folder = folder_path\n","destination_folder = os.getcwd()\n","\n","# fetch all files\n","for file_name in os.listdir(source_folder):\n"," # construct full file path\n"," source = os.path.join(source_folder,file_name)\n"," destination = os.path.join(destination_folder,file_name)\n"," # copy only files\n"," if os.path.isfile(source):\n"," shutil.copy(source, destination)\n"," print('copied', file_name)"]},{"cell_type":"code","source":["BATH_SIZE = 8\n","NUM_EPOCHS = 3\n","MODEL_PATH = 'Luciano/bert-base-portuguese-cased-finetuned-tcu-acordaos'\n","OUTPUT_MODEL_PATH = os.path.join(folder_path,'augmented-bert-portuguese-anatel') + '-last-train'\n","MAX_LENGTH=512\n","EVALUATION_STEPS = 10000\n","HUGGINGFACE_SAVE = 'ennioferreirab/bert-augmented-pt-anatel'\n","#### Just some code to print debug information to stdout\n","logging.basicConfig(format='%(asctime)s - %(message)s',\n"," datefmt='%Y-%m-%d %H:%M:%S',\n"," level=logging.INFO,\n"," handlers=[LoggingHandler()])\n","\n","\n","\n","# Use Huggingface/transformers model (like BERT, RoBERTa, XLNet, XLM-R) for mapping tokens to embeddings\n","word_embedding_model = models.Transformer(MODEL_PATH, max_seq_length=MAX_LENGTH)\n","\n","# Apply mean pooling to get one fixed sized sentence vector\n","pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n","\n","bi_encoder = SentenceTransformer(modules=[word_embedding_model, pooling_model])\n","\n","\n","train_dataloader = DataLoader(train_sample, shuffle=True, batch_size=BATH_SIZE)\n","train_loss = losses.CosineSimilarityLoss(model=bi_encoder)\n","\n","\n","evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_sample, name='sts-test')\n","evaluator.show_progress_bar = True\n","\n","# Configure the training.\n","warmup_steps = math.ceil(len(train_dataloader) * NUM_EPOCHS * 0.1) #10% of train data for warm-up\n","logging.info(\"Warmup-steps: {}\".format(warmup_steps))\n","\n","\n","# Train the bi-encoder model\n","bi_encoder.fit(train_objectives=[(train_dataloader, train_loss)],\n"," evaluator=evaluator,\n"," epochs=NUM_EPOCHS,\n"," evaluation_steps=EVALUATION_STEPS,\n"," warmup_steps=warmup_steps,\n"," output_path=OUTPUT_MODEL_PATH\n"," )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":719,"referenced_widgets":["14c79493badb4c2c973ac11fd03f4fc5","195584b634e043d08501af331b9b7ef7","53b18cc4952146f4a7797cbee17cbca7","60e7cec23870403c9945e6d6f1331fa8","8c06bd0ba28c40a1816a584a98aeae8e","7c01b75128234258b54d01909226b134","03369ae3edc74c2b80eea0425c7f5b56","e25c8721ecc14d528f3423c89d2b6f0d","084feb00d211427c8a57c047182fa879","3b8d16f2078a467eb00f7d111d5b2da7","ed458fb29c184112a470095c4b4171fc","0a7da306f424406190358095a332c7b1","c56ef017ae444abc9639918945befad4","32c783268d794b1a88e07f753c4dadaa","f965651064db48e1beaa08c3d5640f21","91812086383f4fc3980b6965629aac2f","61a3b37554814e08b18954b7b2318875","bf4f5bed5e9a4b66a6336b6a32f9febc","7232a1e563124d83aef7289d2d1e956f","501011f4a9a34763a073377c0e696121","745991fb7f0c4be19f72cdf738582de8","bcf3ccaa418c4655b425fa43d25d2e0d","56496af72d63433ba7c1f67138fd4b09","f64877d47ee249928e1dd652445fa089","090f3e61a31147239a0d7d6dbad5f72f","e6d98a02b2a64c3186dd1699b7cf2a58","e46f1f824b9e4ac3bdc1e9aa94919654","92fb73363ad747e49ffc458abf982484","7075894e74724da2a63ebe08ff14c660","8f532086f1e1498ea1a9ab7ab36147f6","759e136655cc40b6be7833e3c00f720d","fc2861821e124f4f890d89640c3329d0","561409dd080b4485b51c4671dad41b93","c2c16e83aa1b482baa4d30d73c7487f0","38b203f7aa764b35aba89bbaa8eeca92","6b1f37f297cf4a6f8349ecb086d1a0ad","c58c9ee8c8a444d19f2381c7c3245f99","8fef2165e3af44bd81cbb8f5f6b96ef0","cb82ea27bb6c425ea3f8a1437bbc651b","23bdaecdf43b4e458eedb1fbc3bd5503","943a1eb7f955428c9ba4db0f6870d205","c73424a886e04b44b470bae3b49bcaf1","f65c37b47da44fd08857b3e8a5cafbba","79a344d183784f7b90e7adeac3eb1ced","93c47bac887746c9a3e265df88c74e5e","605d3e6dbb2a40998dbb9cc87df587c9","d490d33986a0474dbb03ba64b9264798","7e9316390371442393a77b5b3fceab1d","1a85c9ccf626416fafd08201f5bd3004","e64dcc04bccf4abd9ff84e0ab9c57268","df9c34520f374d14885c792a5b71e5a0","807cf04313ab4dca856cfa0700d18fea","dd3e7e78b1214402b11fe6ce4cd14a6c","683be00992a6438db59dbd2952f04daa","7330056174a54efca7b4e95bb8c3a9b4","b93b5f1662534530b05d2998744d65e2","3f9d0599318e4faabf3bf8eff3a87e0b","6242a8bb90c74090870808153c9aa1cc","079b9dd320204b8e9c88e6c661f11274","05ecd8826b804bd6813fa65b03554e1b","bbe735216330417994aa9cbf17ce8aea","a5636b6883384f98a52c382c29db65af","9a700e3a890241bdac8c7731d23109ad","c153e3ee04c5454f8b01d0b7620b2ac9","b6ff2b74d2944cc181f9b85808a648d6","beb0955ec1b946b0a95c19b5ace617de","8ad4ea6bd4194678a04146b6b8b71fa5","c8eb555a73144020a715887838de5b1f","e6f118c8b8514879a478d38f80cf81f3","a84765def0bd43db8c0ab035ec01cc2b","b7c7a4e2454848bcab040c068305dad7","b48122e7c4704ca684c27f24c7aed0e1","4f6c79bbbbfd4c32bb891127adaebd6e","4cc784026bd844b68a6ca13e834b4fb0","5c0f372095ef49f2a9bcf4114fba8d55","e6ca8b1e20d442658a4539ef306d58ad","07ce3d5edb2147cf96c13436be552661","9756f195a3ee476191ba3ebe2cd0a3b4","79d5111431ad4261bc1c988216c24990","4d9d9c8d2bc8404ca16d4bee62488dcb","9f6dba6cda604c44a3a6384860fff33c","d2bfb4cc8f864361baed6cbc74ae2643","344d46d4ffa94abab5f2ea71b4c200b7","bd2b259a4fbd438eab2df9a99b1019d8","4cbe7b2e4ae84d3dbab1838a155d4a7d","667b695b829b4c9f8b1555ff2690add5","0d14f8dff0474b799ae93f26ab63b8c9","c12587425f2f47179fb8ceae18f9f349"]},"id":"5TFqFOkMMrIX","executionInfo":{"status":"error","timestamp":1659320374322,"user_tz":180,"elapsed":45173,"user":{"displayName":"Ennio Ferreira","userId":"13783596981270154914"}},"outputId":"973232af-175e-4918-b04c-2f5c234e6f5f"},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading config.json: 0%| | 0.00/863 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mevaluation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEVALUATION_STEPS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mwarmup_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarmup_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0moutput_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mOUTPUT_MODEL_PATH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m )\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sentence_transformers/SentenceTransformer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, 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