{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "source": [ "!pip install flair" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "80-pc-7cM_ZK", "outputId": "4317872a-09e6-4a17-8fc1-34981da99bdc" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: flair in /usr/local/lib/python3.10/dist-packages (0.12.2)\n", "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.10/dist-packages (from flair) (2.8.2)\n", "Requirement already satisfied: torch!=1.8,>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from flair) (2.0.0)\n", "Requirement already satisfied: gensim>=3.8.0 in /usr/local/lib/python3.10/dist-packages (from flair) (4.3.2)\n", "Requirement 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requests[socks]->gdown==4.4.0->flair) (2023.7.22)\n", "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown==4.4.0->flair) (1.7.1)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.8,>=1.5.0->flair) (1.3.0)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->transformers[sentencepiece]>=4.18.0->flair) (5.9.5)\n" ] } ] }, { "cell_type": "code", "source": [ "from flair.datasets import CONLL_03\n", "from flair.data import Corpus, Sentence\n", "from flair.datasets import ColumnCorpus\n", "from flair.embeddings import TransformerWordEmbeddings\n", "from flair.models import SequenceTagger\n", "from flair.trainers import ModelTrainer\n", "\n", "import pandas as pd\n", "\n", "from sklearn.model_selection import train_test_split" ], "metadata": { "id": "TQNZ0mBwLriQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "in_path = '/content/drive/MyDrive/Proyecto_ConflictoArmado/NER/output.txt'\n", "\n", "# Abre el archivo de texto en modo lectura\n", "with open(in_path, 'r', encoding='utf-8') as file:\n", " # Lee las líneas del archivo\n", " lines = file.readlines()\n", "\n", "# Inicializa el DataFrame\n", "data = {'sentence_id': [], 'text': [], 'tag': []}\n", "\n", "# Inicializa el identificador de oración\n", "sentence_id = 1\n", "\n", "# Procesa cada línea del archivo\n", "for line in lines:\n", " line = line.strip() # Elimina espacios en blanco al principio y al final de la línea\n", " if line: # Si la línea no está en blanco\n", " parts = line.split() # Divide la línea en palabras y etiquetas\n", " if len(parts) == 2:\n", " word, tag = parts\n", " data['sentence_id'].append(sentence_id)\n", " data['text'].append(word)\n", " data['tag'].append(tag)\n", " else:\n", " # Handle empty lines\n", " data['sentence_id'].append(None)\n", " data['text'].append(None)\n", " data['tag'].append(None)\n", " else:\n", " # Incrementa el identificador de oración al encontrar una línea en blanco\n", " sentence_id += 1\n", "\n", "# Crea el DataFrame\n", "df = pd.DataFrame(data)\n", "\n", "umbral_longitud = 50\n", "\n", "# Filtrar las oraciones por longitud dentro de cada grupo de sentence_id\n", "df_filtrado = df.groupby('sentence_id').filter(lambda group: len(group) < umbral_longitud)\n", "\n", "# Imprimir el DataFrame resultante\n", "df_filtrado" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "eIxuR6GwOwiU", "outputId": "54951a94-56d6-492d-9704-0cecdcd49bf9" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " sentence_id text tag\n", "0 1.0 Naciones B-ORG\n", "1 1.0 Unidas I-ORG\n", "2 1.0 Asamblea O\n", "3 1.0 General O\n", "4 1.0 23 B-DATE\n", "... ... ... ...\n", "88927 658.0 los O\n", "88928 658.0 niños B-PER\n", "88929 658.0 niñas B-PER\n", "88930 658.0 y O\n", "88931 658.0 adolescentes B-PER\n", "\n", "[14485 rows x 3 columns]" ], "text/html": [ "\n", "
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sentence_idtexttag
01.0NacionesB-ORG
11.0UnidasI-ORG
21.0AsambleaO
31.0GeneralO
41.023B-DATE
............
88927658.0losO
88928658.0niñosB-PER
88929658.0niñasB-PER
88930658.0yO
88931658.0adolescentesB-PER
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\n" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "source": [ "to_replace = ['B-'+tag for tag in ['CON', 'ATE', 'MEM', 'LUC', 'EVE', 'DER', 'LEY']]\n", "to_replace.extend(['I-'+tag for tag in ['CON', 'ATE', 'MEM', 'LUC', 'EVE', 'DER', 'LEY']])\n", "df_filtrado['new_tag'] = df_filtrado['tag'].replace(to_replace, 'O')" ], "metadata": { "id": "VH8GZ35KYeYP" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Split the DataFrame into train, test, and dev sets based on sentence_ids\n", "train_ids, test_ids = train_test_split(df_filtrado['sentence_id'].unique(), test_size=0.2, random_state=42)\n", "train_ids, dev_ids = train_test_split(train_ids, test_size=0.1, random_state=42)\n", "\n", "# Define a function to format rows as \"word label\"\n", "def format_row(word, label):\n", " return f\"{word} {label}\"\n", "\n", "# Save data into separate files based on sentence_ids\n", "for split_name, split_ids in [('train', train_ids), ('test', test_ids), ('dev', dev_ids)]:\n", " split_data = df_filtrado[df_filtrado['sentence_id'].isin(split_ids)]\n", "\n", " formatted_rows = []\n", " for _, row in split_data.groupby('sentence_id'):\n", " formatted_rows.extend([format_row(word, label) for word, label in zip(row['text'], row['new_tag'])])\n", " formatted_rows.append(\"\") # Append a blank line after each sentence\n", "\n", " with open(f'{split_name}.txt', 'w') as file:\n", " file.write('\\n'.join(formatted_rows))" ], "metadata": { "id": "svsuyhfWPbn7" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# 1. get the corpus\n", "# define columns\n", "columns = {0 : 'text', 1 : 'ner'}\n", "\n", "# directory where the data resides\n", "data_folder = '/content/'\n", "\n", "# initializing the corpus\n", "corpus: Corpus = ColumnCorpus(data_folder, columns,\n", " train_file = 'train.txt',\n", " test_file = 'test.txt',\n", " dev_file = 'dev.txt')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gpJbWNZUOiOo", "outputId": "c3f21610-2344-4491-99a8-a076fde637a5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:39:04,538 Reading data from /content\n", "2023-09-20 16:39:04,541 Train: /content/train.txt\n", "2023-09-20 16:39:04,542 Dev: /content/dev.txt\n", "2023-09-20 16:39:04,544 Test: /content/test.txt\n" ] } ] }, { "cell_type": "code", "source": [ "# 2. what label do we want to predict?\n", "label_type = 'ner'" ], "metadata": { "id": "Sng99Yx8Oc6d" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# 3. make the label dictionary from the corpus\n", "label_dict = corpus.make_label_dictionary(label_type=label_type, add_unk=False)\n", "print(label_dict)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "F9FDbMdxOdPV", "outputId": "5108697c-0a4c-454b-b423-8c98fac4212a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:39:05,130 Computing label dictionary. Progress:\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "378it [00:00, 12320.75it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:39:05,181 Dictionary created for label 'ner' with 8 values: PER (seen 164 times), GEO (seen 116 times), VIO (seen 113 times), ORG (seen 102 times), ARM (seen 65 times), PAZ (seen 42 times), DATE (seen 40 times), AFE (seen 33 times)\n", "Dictionary with 8 tags: PER, GEO, VIO, ORG, ARM, PAZ, DATE, AFE\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "code", "source": [ "# 4. initialize fine-tuneable transformer embeddings WITH document context\n", "embeddings = TransformerWordEmbeddings(model='xlm-roberta-large',\n", " layers=\"-1\",\n", " subtoken_pooling=\"first\",\n", " fine_tune=True,\n", " use_context=True,\n", " )" ], "metadata": { "id": "5_HyQ1R3QVXE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)\n", "tagger = SequenceTagger(hidden_size=256,\n", " embeddings=embeddings,\n", " tag_dictionary=label_dict,\n", " tag_type='ner',\n", " use_crf=False,\n", " use_rnn=False,\n", " reproject_embeddings=False,\n", " tag_format='BIO')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ycL2ky6DQq_B", "outputId": "2f742b39-39b3-4e81-c616-016b3fc09233" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:39:27,403 SequenceTagger predicts: Dictionary with 17 tags: O, B-PER, I-PER, B-GEO, I-GEO, B-VIO, I-VIO, B-ORG, I-ORG, B-ARM, I-ARM, B-PAZ, I-PAZ, B-DATE, I-DATE, B-AFE, I-AFE\n" ] } ] }, { "cell_type": "code", "source": [ "# 6. initialize trainer\n", "trainer = ModelTrainer(tagger, corpus)" ], "metadata": { "id": "ZscNZ3F1Q2II" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# 7. run fine-tuning\n", "trainer.fine_tune('ner-bertSpanish-fineTuning2',\n", " learning_rate=5.0e-6,\n", " mini_batch_size=4,\n", " mini_batch_chunk_size=1, # remove this parameter to speed up computation if you have a big GPU\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qbyyDWDyQ4QZ", "outputId": "a71a4ee2-2078-4267-d4e3-7782e0c387ee" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:39:27,431 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:27,447 Model: \"SequenceTagger(\n", " (embeddings): TransformerWordEmbeddings(\n", " (model): XLMRobertaModel(\n", " (embeddings): XLMRobertaEmbeddings(\n", " (word_embeddings): Embedding(250003, 1024)\n", " (position_embeddings): Embedding(514, 1024, padding_idx=1)\n", " (token_type_embeddings): Embedding(1, 1024)\n", " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (encoder): XLMRobertaEncoder(\n", " (layer): ModuleList(\n", " (0-23): 24 x XLMRobertaLayer(\n", " (attention): XLMRobertaAttention(\n", " (self): XLMRobertaSelfAttention(\n", " (query): Linear(in_features=1024, out_features=1024, bias=True)\n", " (key): Linear(in_features=1024, out_features=1024, bias=True)\n", " (value): Linear(in_features=1024, out_features=1024, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): XLMRobertaSelfOutput(\n", " (dense): Linear(in_features=1024, out_features=1024, bias=True)\n", " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): XLMRobertaIntermediate(\n", " (dense): Linear(in_features=1024, out_features=4096, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): XLMRobertaOutput(\n", " (dense): Linear(in_features=4096, out_features=1024, bias=True)\n", " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " (pooler): XLMRobertaPooler(\n", " (dense): Linear(in_features=1024, out_features=1024, bias=True)\n", " (activation): Tanh()\n", " )\n", " )\n", " )\n", " (locked_dropout): LockedDropout(p=0.5)\n", " (linear): Linear(in_features=1024, out_features=17, bias=True)\n", " (loss_function): CrossEntropyLoss()\n", ")\"\n", "2023-09-20 16:39:27,450 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:27,454 Corpus: \"Corpus: 378 train + 42 dev + 106 test sentences\"\n", "2023-09-20 16:39:27,455 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:27,458 Parameters:\n", "2023-09-20 16:39:27,461 - learning_rate: \"0.000005\"\n", "2023-09-20 16:39:27,471 - mini_batch_size: \"4\"\n", "2023-09-20 16:39:27,481 - patience: \"3\"\n", "2023-09-20 16:39:27,483 - anneal_factor: \"0.5\"\n", "2023-09-20 16:39:27,488 - max_epochs: \"10\"\n", "2023-09-20 16:39:27,492 - shuffle: \"True\"\n", "2023-09-20 16:39:27,499 - train_with_dev: \"False\"\n", "2023-09-20 16:39:27,501 - batch_growth_annealing: \"False\"\n", "2023-09-20 16:39:27,504 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:27,506 Model training base path: \"ner-bertSpanish-fineTuning2\"\n", "2023-09-20 16:39:27,509 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:27,512 Device: cuda:0\n", "2023-09-20 16:39:27,515 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:27,517 Embeddings storage mode: none\n", "2023-09-20 16:39:27,521 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:39:36,397 epoch 1 - iter 9/95 - loss 3.34492128 - time (sec): 8.87 - samples/sec: 103.44 - lr: 0.000000\n", "2023-09-20 16:39:46,525 epoch 1 - iter 18/95 - loss 3.28965828 - time (sec): 19.00 - samples/sec: 92.20 - lr: 0.000001\n", "2023-09-20 16:39:53,173 epoch 1 - iter 27/95 - loss 3.24176297 - time (sec): 25.65 - samples/sec: 102.41 - lr: 0.000001\n", "2023-09-20 16:40:00,001 epoch 1 - iter 36/95 - loss 3.24931554 - time (sec): 32.48 - samples/sec: 110.47 - lr: 0.000002\n", "2023-09-20 16:40:07,278 epoch 1 - iter 45/95 - loss 3.21247192 - time (sec): 39.76 - samples/sec: 113.74 - lr: 0.000002\n", "2023-09-20 16:40:13,757 epoch 1 - iter 54/95 - loss 3.13623468 - time (sec): 46.23 - samples/sec: 122.48 - lr: 0.000003\n", "2023-09-20 16:40:20,791 epoch 1 - iter 63/95 - loss 2.98242809 - time (sec): 53.27 - samples/sec: 127.69 - lr: 0.000003\n", "2023-09-20 16:40:27,323 epoch 1 - iter 72/95 - loss 2.71884308 - time (sec): 59.80 - samples/sec: 131.47 - lr: 0.000004\n", "2023-09-20 16:40:33,896 epoch 1 - iter 81/95 - loss 2.45957049 - time (sec): 66.37 - samples/sec: 133.97 - lr: 0.000004\n", "2023-09-20 16:40:40,825 epoch 1 - iter 90/95 - loss 2.29787318 - time (sec): 73.30 - samples/sec: 134.89 - lr: 0.000005\n", "2023-09-20 16:40:44,103 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:40:44,105 EPOCH 1 done: loss 2.2083 - lr 0.000005\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.94it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:40:46,340 Evaluating as a multi-label problem: False\n", "2023-09-20 16:40:46,359 DEV : loss 0.6564602851867676 - f1-score (micro avg) 0.0\n", "2023-09-20 16:40:46,363 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:40:52,692 epoch 2 - iter 9/95 - loss 0.68008748 - time (sec): 6.33 - samples/sec: 164.03 - lr: 0.000005\n", "2023-09-20 16:40:59,736 epoch 2 - iter 18/95 - loss 0.82214551 - time (sec): 13.37 - samples/sec: 150.47 - lr: 0.000005\n", "2023-09-20 16:41:06,257 epoch 2 - iter 27/95 - loss 0.84081419 - time (sec): 19.89 - samples/sec: 151.82 - lr: 0.000005\n", "2023-09-20 16:41:13,054 epoch 2 - iter 36/95 - loss 0.82311766 - time (sec): 26.69 - samples/sec: 149.50 - lr: 0.000005\n", "2023-09-20 16:41:21,216 epoch 2 - iter 45/95 - loss 0.85481312 - time (sec): 34.85 - samples/sec: 144.73 - lr: 0.000005\n", "2023-09-20 16:41:27,552 epoch 2 - iter 54/95 - loss 0.83370049 - time (sec): 41.19 - samples/sec: 143.63 - lr: 0.000005\n", "2023-09-20 16:41:34,547 epoch 2 - iter 63/95 - loss 0.81702753 - time (sec): 48.18 - samples/sec: 141.57 - lr: 0.000005\n", "2023-09-20 16:41:40,865 epoch 2 - iter 72/95 - loss 0.83400647 - time (sec): 54.50 - samples/sec: 142.35 - lr: 0.000005\n", "2023-09-20 16:41:47,553 epoch 2 - iter 81/95 - loss 0.81831942 - time (sec): 61.19 - samples/sec: 143.43 - lr: 0.000005\n", "2023-09-20 16:41:54,534 epoch 2 - iter 90/95 - loss 0.80080903 - time (sec): 68.17 - samples/sec: 144.46 - lr: 0.000004\n", "2023-09-20 16:41:58,032 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:41:58,033 EPOCH 2 done: loss 0.7905 - lr 0.000004\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.83it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:42:00,319 Evaluating as a multi-label problem: False\n", "2023-09-20 16:42:00,334 DEV : loss 0.5198074579238892 - f1-score (micro avg) 0.0\n", "2023-09-20 16:42:00,337 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:42:06,878 epoch 3 - iter 9/95 - loss 0.57110353 - time (sec): 6.54 - samples/sec: 160.02 - lr: 0.000004\n", "2023-09-20 16:42:13,524 epoch 3 - iter 18/95 - loss 0.62175665 - time (sec): 13.18 - samples/sec: 147.39 - lr: 0.000004\n", "2023-09-20 16:42:20,138 epoch 3 - iter 27/95 - loss 0.57933822 - time (sec): 19.80 - samples/sec: 149.56 - lr: 0.000004\n", "2023-09-20 16:42:27,222 epoch 3 - iter 36/95 - loss 0.59631046 - time (sec): 26.88 - samples/sec: 150.18 - lr: 0.000004\n", "2023-09-20 16:42:33,797 epoch 3 - iter 45/95 - loss 0.58896606 - time (sec): 33.46 - samples/sec: 149.93 - lr: 0.000004\n", "2023-09-20 16:42:40,231 epoch 3 - iter 54/95 - loss 0.60859906 - time (sec): 39.89 - samples/sec: 150.41 - lr: 0.000004\n", "2023-09-20 16:42:47,408 epoch 3 - iter 63/95 - loss 0.63216711 - time (sec): 47.07 - samples/sec: 147.00 - lr: 0.000004\n", "2023-09-20 16:42:53,559 epoch 3 - iter 72/95 - loss 0.63824288 - time (sec): 53.22 - samples/sec: 148.41 - lr: 0.000004\n", "2023-09-20 16:43:00,419 epoch 3 - iter 81/95 - loss 0.67740427 - time (sec): 60.08 - samples/sec: 149.91 - lr: 0.000004\n", "2023-09-20 16:43:07,400 epoch 3 - iter 90/95 - loss 0.68385038 - time (sec): 67.06 - samples/sec: 148.69 - lr: 0.000004\n", "2023-09-20 16:43:10,660 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:43:10,662 EPOCH 3 done: loss 0.6759 - lr 0.000004\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.86it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:43:12,936 Evaluating as a multi-label problem: False\n", "2023-09-20 16:43:12,953 DEV : loss 0.4649089276790619 - f1-score (micro avg) 0.0\n", "2023-09-20 16:43:12,958 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:43:19,654 epoch 4 - iter 9/95 - loss 0.68437438 - time (sec): 6.69 - samples/sec: 153.56 - lr: 0.000004\n", "2023-09-20 16:43:26,266 epoch 4 - iter 18/95 - loss 0.61813965 - time (sec): 13.31 - samples/sec: 153.23 - lr: 0.000004\n", "2023-09-20 16:43:32,808 epoch 4 - iter 27/95 - loss 0.61204996 - time (sec): 19.85 - samples/sec: 157.44 - lr: 0.000004\n", "2023-09-20 16:43:40,029 epoch 4 - iter 36/95 - loss 0.61660487 - time (sec): 27.07 - samples/sec: 151.61 - lr: 0.000004\n", "2023-09-20 16:43:46,462 epoch 4 - iter 45/95 - loss 0.60674645 - time (sec): 33.50 - samples/sec: 149.72 - lr: 0.000004\n", "2023-09-20 16:43:53,219 epoch 4 - iter 54/95 - loss 0.62906709 - time (sec): 40.26 - samples/sec: 148.66 - lr: 0.000004\n", "2023-09-20 16:44:00,505 epoch 4 - iter 63/95 - loss 0.61740208 - time (sec): 47.55 - samples/sec: 148.32 - lr: 0.000004\n", "2023-09-20 16:44:06,796 epoch 4 - iter 72/95 - loss 0.62528363 - time (sec): 53.84 - samples/sec: 147.19 - lr: 0.000003\n", "2023-09-20 16:44:13,495 epoch 4 - iter 81/95 - loss 0.62469252 - time (sec): 60.54 - samples/sec: 149.07 - lr: 0.000003\n", "2023-09-20 16:44:20,347 epoch 4 - iter 90/95 - loss 0.62439226 - time (sec): 67.39 - samples/sec: 148.01 - lr: 0.000003\n", "2023-09-20 16:44:23,654 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:44:23,656 EPOCH 4 done: loss 0.6175 - lr 0.000003\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.87it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:44:25,928 Evaluating as a multi-label problem: False\n", "2023-09-20 16:44:25,946 DEV : loss 0.4446188509464264 - f1-score (micro avg) 0.0\n", "2023-09-20 16:44:25,949 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:44:32,764 epoch 5 - iter 9/95 - loss 0.68330165 - time (sec): 6.81 - samples/sec: 149.71 - lr: 0.000003\n", "2023-09-20 16:44:39,414 epoch 5 - iter 18/95 - loss 0.55349558 - time (sec): 13.46 - samples/sec: 145.36 - lr: 0.000003\n", "2023-09-20 16:44:45,683 epoch 5 - iter 27/95 - loss 0.55279158 - time (sec): 19.73 - samples/sec: 147.63 - lr: 0.000003\n", "2023-09-20 16:44:52,989 epoch 5 - iter 36/95 - loss 0.53164896 - time (sec): 27.04 - samples/sec: 144.95 - lr: 0.000003\n", "2023-09-20 16:44:59,509 epoch 5 - iter 45/95 - loss 0.57837004 - time (sec): 33.56 - samples/sec: 147.21 - lr: 0.000003\n", "2023-09-20 16:45:06,162 epoch 5 - iter 54/95 - loss 0.54499535 - time (sec): 40.21 - samples/sec: 150.13 - lr: 0.000003\n", "2023-09-20 16:45:13,305 epoch 5 - iter 63/95 - loss 0.55937756 - time (sec): 47.35 - samples/sec: 148.82 - lr: 0.000003\n", "2023-09-20 16:45:19,656 epoch 5 - iter 72/95 - loss 0.56013288 - time (sec): 53.70 - samples/sec: 149.84 - lr: 0.000003\n", "2023-09-20 16:45:26,507 epoch 5 - iter 81/95 - loss 0.54816732 - time (sec): 60.56 - samples/sec: 149.07 - lr: 0.000003\n", "2023-09-20 16:45:33,154 epoch 5 - iter 90/95 - loss 0.53285600 - time (sec): 67.20 - samples/sec: 148.39 - lr: 0.000003\n", "2023-09-20 16:45:36,568 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:45:36,569 EPOCH 5 done: loss 0.5402 - lr 0.000003\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.85it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:45:38,846 Evaluating as a multi-label problem: False\n", "2023-09-20 16:45:38,866 DEV : loss 0.4345150291919708 - f1-score (micro avg) 0.0256\n", "2023-09-20 16:45:38,870 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:45:45,695 epoch 6 - iter 9/95 - loss 0.52923392 - time (sec): 6.82 - samples/sec: 145.11 - lr: 0.000003\n", "2023-09-20 16:45:51,985 epoch 6 - iter 18/95 - loss 0.51792781 - time (sec): 13.11 - samples/sec: 156.03 - lr: 0.000003\n", "2023-09-20 16:45:58,393 epoch 6 - iter 27/95 - loss 0.49117250 - time (sec): 19.52 - samples/sec: 151.07 - lr: 0.000003\n", "2023-09-20 16:46:05,614 epoch 6 - iter 36/95 - loss 0.50167473 - time (sec): 26.74 - samples/sec: 147.26 - lr: 0.000003\n", "2023-09-20 16:46:11,980 epoch 6 - iter 45/95 - loss 0.47179564 - time (sec): 33.11 - samples/sec: 149.51 - lr: 0.000003\n", "2023-09-20 16:46:18,861 epoch 6 - iter 54/95 - loss 0.45136159 - time (sec): 39.99 - samples/sec: 152.32 - lr: 0.000002\n", "2023-09-20 16:46:25,844 epoch 6 - iter 63/95 - loss 0.47423503 - time (sec): 46.97 - samples/sec: 149.30 - lr: 0.000002\n", "2023-09-20 16:46:32,126 epoch 6 - iter 72/95 - loss 0.47496461 - time (sec): 53.25 - samples/sec: 150.66 - lr: 0.000002\n", "2023-09-20 16:46:39,170 epoch 6 - iter 81/95 - loss 0.49848349 - time (sec): 60.30 - samples/sec: 148.28 - lr: 0.000002\n", "2023-09-20 16:46:45,744 epoch 6 - iter 90/95 - loss 0.49210164 - time (sec): 66.87 - samples/sec: 148.99 - lr: 0.000002\n", "2023-09-20 16:46:49,149 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:46:49,150 EPOCH 6 done: loss 0.4930 - lr 0.000002\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.88it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:46:51,414 Evaluating as a multi-label problem: False\n", "2023-09-20 16:46:51,428 DEV : loss 0.38467857241630554 - f1-score (micro avg) 0.2286\n", "2023-09-20 16:46:51,432 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:46:58,494 epoch 7 - iter 9/95 - loss 0.40116530 - time (sec): 7.06 - samples/sec: 149.71 - lr: 0.000002\n", "2023-09-20 16:47:04,965 epoch 7 - iter 18/95 - loss 0.45271521 - time (sec): 13.53 - samples/sec: 153.11 - lr: 0.000002\n", "2023-09-20 16:47:11,575 epoch 7 - iter 27/95 - loss 0.45269619 - time (sec): 20.14 - samples/sec: 153.41 - lr: 0.000002\n", "2023-09-20 16:47:18,725 epoch 7 - iter 36/95 - loss 0.44785464 - time (sec): 27.29 - samples/sec: 150.23 - lr: 0.000002\n", "2023-09-20 16:47:25,036 epoch 7 - iter 45/95 - loss 0.41670820 - time (sec): 33.60 - samples/sec: 149.19 - lr: 0.000002\n", "2023-09-20 16:47:31,759 epoch 7 - iter 54/95 - loss 0.44843613 - time (sec): 40.33 - samples/sec: 151.34 - lr: 0.000002\n", "2023-09-20 16:47:38,802 epoch 7 - iter 63/95 - loss 0.46794764 - time (sec): 47.37 - samples/sec: 150.33 - lr: 0.000002\n", "2023-09-20 16:47:45,076 epoch 7 - iter 72/95 - loss 0.46597196 - time (sec): 53.64 - samples/sec: 149.90 - lr: 0.000002\n", "2023-09-20 16:47:52,339 epoch 7 - iter 81/95 - loss 0.46348741 - time (sec): 60.91 - samples/sec: 149.61 - lr: 0.000002\n", "2023-09-20 16:47:59,025 epoch 7 - iter 90/95 - loss 0.45404421 - time (sec): 67.59 - samples/sec: 147.47 - lr: 0.000002\n", "2023-09-20 16:48:02,689 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:48:02,690 EPOCH 7 done: loss 0.4593 - lr 0.000002\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.78it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:48:05,006 Evaluating as a multi-label problem: False\n", "2023-09-20 16:48:05,029 DEV : loss 0.3640497028827667 - f1-score (micro avg) 0.2222\n", "2023-09-20 16:48:05,036 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:48:12,140 epoch 8 - iter 9/95 - loss 0.38261461 - time (sec): 7.10 - samples/sec: 153.05 - lr: 0.000002\n", "2023-09-20 16:48:18,934 epoch 8 - iter 18/95 - loss 0.35649288 - time (sec): 13.90 - samples/sec: 151.41 - lr: 0.000002\n", "2023-09-20 16:48:25,579 epoch 8 - iter 27/95 - loss 0.39724913 - time (sec): 20.54 - samples/sec: 152.04 - lr: 0.000002\n", "2023-09-20 16:48:32,424 epoch 8 - iter 36/95 - loss 0.41548796 - time (sec): 27.39 - samples/sec: 149.82 - lr: 0.000001\n", "2023-09-20 16:48:38,844 epoch 8 - iter 45/95 - loss 0.38478886 - time (sec): 33.81 - samples/sec: 149.47 - lr: 0.000001\n", "2023-09-20 16:48:45,816 epoch 8 - iter 54/95 - loss 0.39225607 - time (sec): 40.78 - samples/sec: 148.39 - lr: 0.000001\n", "2023-09-20 16:48:52,568 epoch 8 - iter 63/95 - loss 0.37744603 - time (sec): 47.53 - samples/sec: 148.94 - lr: 0.000001\n", "2023-09-20 16:48:59,082 epoch 8 - iter 72/95 - loss 0.37978610 - time (sec): 54.04 - samples/sec: 146.72 - lr: 0.000001\n", "2023-09-20 16:49:06,514 epoch 8 - iter 81/95 - loss 0.39005255 - time (sec): 61.48 - samples/sec: 144.29 - lr: 0.000001\n", "2023-09-20 16:49:14,188 epoch 8 - iter 90/95 - loss 0.39827420 - time (sec): 69.15 - samples/sec: 143.73 - lr: 0.000001\n", "2023-09-20 16:49:17,638 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:49:17,639 EPOCH 8 done: loss 0.4061 - lr 0.000001\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.85it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:49:19,919 Evaluating as a multi-label problem: False\n", "2023-09-20 16:49:19,937 DEV : loss 0.3643392324447632 - f1-score (micro avg) 0.2313\n", "2023-09-20 16:49:19,941 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:49:26,803 epoch 9 - iter 9/95 - loss 0.37628733 - time (sec): 6.86 - samples/sec: 157.70 - lr: 0.000001\n", "2023-09-20 16:49:33,861 epoch 9 - iter 18/95 - loss 0.34309120 - time (sec): 13.92 - samples/sec: 146.92 - lr: 0.000001\n", "2023-09-20 16:49:40,349 epoch 9 - iter 27/95 - loss 0.36862108 - time (sec): 20.41 - samples/sec: 149.51 - lr: 0.000001\n", "2023-09-20 16:49:47,393 epoch 9 - iter 36/95 - loss 0.37854449 - time (sec): 27.45 - samples/sec: 146.22 - lr: 0.000001\n", "2023-09-20 16:49:54,443 epoch 9 - iter 45/95 - loss 0.38543222 - time (sec): 34.50 - samples/sec: 142.90 - lr: 0.000001\n", "2023-09-20 16:50:01,292 epoch 9 - iter 54/95 - loss 0.37676979 - time (sec): 41.35 - samples/sec: 143.53 - lr: 0.000001\n", "2023-09-20 16:50:08,679 epoch 9 - iter 63/95 - loss 0.37714998 - time (sec): 48.74 - samples/sec: 141.62 - lr: 0.000001\n", "2023-09-20 16:50:15,118 epoch 9 - iter 72/95 - loss 0.37534464 - time (sec): 55.18 - samples/sec: 143.76 - lr: 0.000001\n", "2023-09-20 16:50:21,880 epoch 9 - iter 81/95 - loss 0.37937109 - time (sec): 61.94 - samples/sec: 143.52 - lr: 0.000001\n", "2023-09-20 16:50:29,147 epoch 9 - iter 90/95 - loss 0.37920452 - time (sec): 69.20 - samples/sec: 143.50 - lr: 0.000001\n", "2023-09-20 16:50:32,552 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:50:32,553 EPOCH 9 done: loss 0.3825 - lr 0.000001\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.82it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:50:34,849 Evaluating as a multi-label problem: False\n", "2023-09-20 16:50:34,864 DEV : loss 0.3552079200744629 - f1-score (micro avg) 0.2252\n", "2023-09-20 16:50:34,868 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:50:41,507 epoch 10 - iter 9/95 - loss 0.23978793 - time (sec): 6.64 - samples/sec: 164.84 - lr: 0.000001\n", "2023-09-20 16:50:48,591 epoch 10 - iter 18/95 - loss 0.26464288 - time (sec): 13.72 - samples/sec: 146.78 - lr: 0.000000\n", "2023-09-20 16:50:54,870 epoch 10 - iter 27/95 - loss 0.30564030 - time (sec): 20.00 - samples/sec: 154.20 - lr: 0.000000\n", "2023-09-20 16:51:02,035 epoch 10 - iter 36/95 - loss 0.32322471 - time (sec): 27.17 - samples/sec: 146.51 - lr: 0.000000\n", "2023-09-20 16:51:08,927 epoch 10 - iter 45/95 - loss 0.34168939 - time (sec): 34.06 - samples/sec: 144.14 - lr: 0.000000\n", "2023-09-20 16:51:15,553 epoch 10 - iter 54/95 - loss 0.34366191 - time (sec): 40.68 - samples/sec: 146.10 - lr: 0.000000\n", "2023-09-20 16:51:22,584 epoch 10 - iter 63/95 - loss 0.33181748 - time (sec): 47.71 - samples/sec: 144.84 - lr: 0.000000\n", "2023-09-20 16:51:29,148 epoch 10 - iter 72/95 - loss 0.34679468 - time (sec): 54.28 - samples/sec: 145.27 - lr: 0.000000\n", "2023-09-20 16:51:35,756 epoch 10 - iter 81/95 - loss 0.35531872 - time (sec): 60.89 - samples/sec: 145.27 - lr: 0.000000\n", "2023-09-20 16:51:42,815 epoch 10 - iter 90/95 - loss 0.36415594 - time (sec): 67.94 - samples/sec: 145.62 - lr: 0.000000\n", "2023-09-20 16:51:46,067 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:51:46,068 EPOCH 10 done: loss 0.3679 - lr 0.000000\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 11/11 [00:02<00:00, 4.83it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:51:48,360 Evaluating as a multi-label problem: False\n", "2023-09-20 16:51:48,376 DEV : loss 0.35039085149765015 - f1-score (micro avg) 0.2313\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:51:58,375 ----------------------------------------------------------------------------------------------------\n", "2023-09-20 16:51:58,380 Testing using last state of model ...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 27/27 [00:05<00:00, 5.28it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "2023-09-20 16:52:03,500 Evaluating as a multi-label problem: False\n", "2023-09-20 16:52:03,518 0.367\t0.3941\t0.38\t0.2424\n", "2023-09-20 16:52:03,519 \n", "Results:\n", "- F-score (micro) 0.38\n", "- F-score (macro) 0.271\n", "- Accuracy 0.2424\n", "\n", "By class:\n", " precision recall f1-score support\n", "\n", " PER 0.2029 0.3256 0.2500 43\n", " VIO 0.4894 0.6216 0.5476 37\n", " ORG 0.4524 0.5429 0.4935 35\n", " GEO 0.4222 0.6333 0.5067 30\n", " ARM 0.5556 0.2778 0.3704 18\n", " PAZ 0.0000 0.0000 0.0000 19\n", " AFE 0.0000 0.0000 0.0000 11\n", " DATE 0.0000 0.0000 0.0000 10\n", "\n", " micro avg 0.3670 0.3941 0.3800 203\n", " macro avg 0.2653 0.3001 0.2710 203\n", "weighted avg 0.3218 0.3941 0.3456 203\n", "\n", "2023-09-20 16:52:03,521 ----------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "{'test_score': 0.3800475059382424,\n", " 'dev_score_history': [0.0,\n", " 0.0,\n", " 0.0,\n", " 0.0,\n", " 0.02564102564102564,\n", " 0.2285714285714286,\n", " 0.22222222222222224,\n", " 0.23129251700680276,\n", " 0.2251655629139073,\n", " 0.23129251700680276],\n", " 'train_loss_history': [2.2083096445195376,\n", " 0.7905150630989667,\n", " 0.6759139882288138,\n", " 0.6174707539115155,\n", " 0.5402019214430303,\n", " 0.49300472509990656,\n", " 0.4592541911305255,\n", " 0.4060959751833173,\n", " 0.3825031868616195,\n", " 0.3679287539225993],\n", " 'dev_loss_history': [0.6564602851867676,\n", " 0.5198074579238892,\n", " 0.4649089276790619,\n", " 0.4446188509464264,\n", " 0.4345150291919708,\n", " 0.38467857241630554,\n", " 0.3640497028827667,\n", " 0.3643392324447632,\n", " 0.3552079200744629,\n", " 0.35039085149765015]}" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "# load the trained model\n", "model = SequenceTagger.load('/content/resources/taggers/sota-ner-flert/final-model.pt')\n", "\n", "# create example sentence\n", "sentence = Sentence('Los principales perpetradores de esta violación fueron los paramilitares seguidos de guerrillas como las FARC y el ELN y fuerza pública y otros agentes del Estado como los organismos de inteligencia')# predict the tags\n", "sentence2 = Sentence('Hola. Mi nombre es David Cortés, tengo 27 años de edad y soy de Tumaco, Nariño. Llegué a Bogotá cuando tenía 17 años por temas de seguridad. Mi mamá me envío a esta ciudad porque mi vida estaba en riesgo luego de que, por accidente, fuera testigo de la muerte de un narcotraficante. Todo el barrio estaba lleno de narcos')\n", "sentence3 = Sentence('Mi mamá me envío a esta ciudad porque mi vida estaba en riesgo luego de que, por accidente, fuera testigo de la muerte de un narcotraficante. Todo el barrio estaba lleno de narcos')\n", "model.predict(sentence)\n", "model.predict(sentence2)\n", "model.predict(sentence3)\n", "\n", "print(sentence.to_tagged_string())\n", "print(sentence2.to_tagged_string())\n", "print(sentence3.to_tagged_string())" ], "metadata": { "id": "l0dN82neXCdp" }, "execution_count": null, "outputs": [] } ] }