![]() Print(classification_report_seqeval(y_true, y_pred)) Print the classification report: from trics import classification_report as classification_report_seqeval Y_pred = read_examples_from_file("spanberta-ner/test_predictions.txt") Read data and labels from the raw text files: y_true = read_examples_from_file("test.txt") # Examples could have no label for mode = "test" If line.startswith("-DOCSTART-") or line = "" or line = "\n": With open(file_path, encoding="utf-8") as f: Holding words in each sequence, and labels (list of lists) holding """Read words and labels from a CoNLL-2002/2003 data file.Įxamples (dict): a dictionary with two keys: words (list of lists) To understand how well our model actually performs, let's load its predictions and examine the classification report. ![]() We can see that the models overfit the training data after 3 epoches. Here are the tensorboards of fine-tuning spanberta and bert-base-multilingual-cased for 5 epoches. Performance on the test set: 02:24:48 - INFO - _main_ - ***** Eval results ***** Performance on the dev set: 02:24:31 - INFO - _main_ - ***** Eval results ***** Let’s define some variables that we need for further pre-processing steps and training the model: MAX_LENGTH = 120 !cat conll2002/esp.testb | cut -d " " -f 1,3 > test_temp.txt !cat conll2002/esp.testa | cut -d " " -f 1,3 > dev_temp.txt ![]() !cat conll2002/esp.train | cut -d " " -f 1,3 > train_temp.txt We will only keep the word column and the named entity tag column for our train, dev and test datasets. Sentence breaks are encoded by empty lines. ![]() The size of each dataset: !wc -l conll2002/esp.trainĪll data files has three columns: words, associated part-of-speech tags and named entity tags in the IOB2 format. esp.testa: Spanish test data for the development stage.The files contain the train and test data for three parts of the CoNLL-2002 shared task: The below command will download and unzip the dataset. Setupĭownload transformers and install required packages. We will use the script run_ner.py by Hugging Face and CoNLL-2002 dataset to fine-tune SpanBERTa. In this blog post, to really leverage the power of transformer models, we will fine-tune SpanBERTa for a named-entity recognition task.Īccording to its definition on Wikipedia, Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entity mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. The model has shown to be able to predict correctly masked words in a sequence based on its context. In my previous blog post, we have discussed how my team pretrained SpanBERTa, a transformer language model for Spanish, on a big corpus from scratch. Part III: How to Train an ELECTRA Language Model for Spanish from Scratch.Part I: How to Train a RoBERTa Language Model for Spanish from Scratch.It is Part II of III in a series on training custom BERT Language Models for Spanish for a variety of use cases: Specifically, how to train a BERT variation, SpanBERTa, for NER. This article is on how to fine-tune BERT for Named Entity Recognition (NER).
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