Nahrát soubory do „pages/students/2016/jakub_maruniak/dp2021/annotation“
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"""Scripts used for training and evaluation of NER models
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Usage example:
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$ python custom_train.py train ./model ./train.jsonl ./eval.jsonl -o ./output_dir -n 15
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Requirements:
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spacy>=2.2.3
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https://github.com/explosion/projects/tree/master/ner-drugs
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"""
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import spacy
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from spacy.cli.train import _load_pretrained_tok2vec
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from timeit import default_timer as timer
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from pathlib import Path
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import srsly
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from wasabi import msg
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import random
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import plac
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import sys
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import tqdm
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def format_data(data):
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result = []
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labels = set()
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for eg in data:
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if eg["answer"] != "accept":
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continue
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ents = [(s["start"], s["end"], s["label"]) for s in eg.get("spans", [])]
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labels.update([ent[2] for ent in ents])
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result.append((eg["text"], {"entities": ents}))
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return result, labels
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@plac.annotations(
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model=("The base model to load or blank:lang", "positional", None, str),
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train_path=("The training data (Prodigy JSONL)", "positional", None, str),
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eval_path=("The evaluation data (Prodigy JSONL)", "positional", None, str),
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n_iter=("Number of iterations", "option", "n", int),
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output=("Optional output directory", "option", "o", str),
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tok2vec=("Pretrained tok2vec weights to initialize model", "option", "t2v", str),
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)
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def train_model(
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model, train_path, eval_path, n_iter=10, output=None, tok2vec=None,
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):
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"""
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Train a model from Prodigy annotations and optionally save out the best
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model to disk.
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"""
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spacy.util.fix_random_seed(0)
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with msg.loading(f"Loading '{model}'..."):
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if model.startswith("blank:"):
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nlp = spacy.blank(model.replace("blank:", ""))
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else:
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nlp = spacy.load(model)
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msg.good(f"Loaded model '{model}'")
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train_data, labels = format_data(srsly.read_jsonl(train_path))
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eval_data, _ = format_data(srsly.read_jsonl(eval_path))
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ner = nlp.create_pipe("ner")
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for label in labels:
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ner.add_label(label)
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nlp.add_pipe(ner)
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t2v_cfg = {
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"embed_rows": 10000,
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"token_vector_width": 128,
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"conv_depth": 8,
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"nr_feature_tokens": 3,
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}
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optimizer = nlp.begin_training(component_cfg={"ner": t2v_cfg} if tok2vec else {})
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if tok2vec:
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_load_pretrained_tok2vec(nlp, Path(tok2vec))
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batch_size = spacy.util.compounding(1.0, 32.0, 1.001)
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best_acc = 0
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best_model = None
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row_widths = (2, 8, 8, 8, 8)
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msg.row(("#", "L", "P", "R", "F"), widths=row_widths)
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for i in range(n_iter):
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random.shuffle(train_data)
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losses = {}
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data = tqdm.tqdm(train_data, leave=False)
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for batch in spacy.util.minibatch(data, size=batch_size):
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texts, annots = zip(*batch)
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nlp.update(texts, annots, drop=0.2, losses=losses)
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with nlp.use_params(optimizer.averages):
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sc = nlp.evaluate(eval_data)
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if sc.ents_f > best_acc:
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best_acc = sc.ents_f
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if output:
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best_model = nlp.to_bytes()
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acc = (f"{sc.ents_p:.3f}", f"{sc.ents_r:.3f}", f"{sc.ents_f:.3f}")
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msg.row((i + 1, f"{losses['ner']:.2f}", *acc), widths=row_widths)
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msg.text(f"Best F-Score: {best_acc:.3f}")
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if output and best_model:
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with msg.loading("Saving model..."):
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nlp.from_bytes(best_model).to_disk(output)
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msg.good("Saved model", output)
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@plac.annotations(
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model=("The model to evaluate", "positional", None, str),
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eval_path=("The evaluation data (Prodigy JSONL)", "positional", None, str),
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)
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def evaluate_model(model, eval_path):
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"""
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Evaluate a trained model on Prodigy annotations and print the accuracy.
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"""
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with msg.loading(f"Loading model '{model}'..."):
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nlp = spacy.load(model)
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data, _ = format_data(srsly.read_jsonl(eval_path))
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sc = nlp.evaluate(data)
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result = [
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("Precision", f"{sc.ents_p:.3f}"),
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("Recall", f"{sc.ents_r:.3f}"),
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("F-Score", f"{sc.ents_f:.3f}"),
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]
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msg.table(result)
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@plac.annotations(
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model=("The model to evaluate", "positional", None, str),
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data=("Raw data as JSONL", "positional", None, str),
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)
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def wps(model, data):
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"""
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Measure the processing speed in words per second. It's recommended to
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use a larger corpus of raw text here (e.g. a few million words).
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"""
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with msg.loading(f"Loading model '{model}'..."):
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nlp = spacy.load(model)
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texts = (eg["text"] for eg in srsly.read_jsonl(data))
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n_docs = 0
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n_words = 0
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start_time = timer()
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for doc in nlp.pipe(texts):
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n_docs += 1
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n_words += len(doc)
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end_time = timer()
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wps = int(n_words / (end_time - start_time))
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result = [
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("Docs", f"{n_docs:,}"),
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("Words", f"{n_words:,}"),
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("Words/s", f"{wps:,}"),
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]
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msg.table(result, widths=(7, 12), aligns=("l", "r"))
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if __name__ == "__main__":
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opts = {"train": train_model, "evaluate": evaluate_model, "wps": wps}
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cmd = sys.argv.pop(1)
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if cmd not in opts:
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msg.fail(f"Unknown command: {cmd}", f"Available: {', '.join(opts)}", exits=1)
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try:
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plac.call(opts[cmd])
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except KeyboardInterrupt:
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msg.warn("Stopped.", exits=1)
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160
pages/students/2016/jakub_maruniak/dp2021/annotation/scripts.py
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160
pages/students/2016/jakub_maruniak/dp2021/annotation/scripts.py
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"""
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Usage example:
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$ python scripts.py delete_annot jakub.maruniak ./dataset.jsonl ./new_dataset.jsonl
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To see available commands:
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$ python scripts.py help
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To see available arguments:
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$ python scripts.py [command] --help
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"""
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import spacy
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import srsly
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from wasabi import msg
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import plac
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import sys
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import re
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import itertools
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@plac.annotations(
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dataset_path=("Path to dataset (Prodigy JSONL format)", "positional", None, str),
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)
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def count(
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dataset_path
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):
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"""
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Print statistics about Prodigy JSONL dataset.
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Prints number of accepted, rejected and ignored articles.
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Prints number of annotations of each entity type.
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Prints how much annotations were made by each annotator.
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"""
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# load data
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# filename = 'ner/skner/sknerv4spans.jsonl'
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file = open(sys.argv[1], 'rt', encoding='utf-8')
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text = file.read()
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# count articles
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countAccept = text.count('accept')
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countReject = text.count('reject')
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countSkip = text.count('ignore')
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countSpans = text.count('tokens')
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# count entities
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countPER = text.count('PER')
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countLOC = text.count('LOC')
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countORG = text.count('ORG')
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countMISC = text.count('MISC')
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underline = '\033[04m'
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reset = '\033[0m'
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red = '\033[31m'
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green='\033[32m'
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gray='\033[37m'
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# table v1
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#from lib import TableIt
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#table1 = [
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# ['Prijatých', countAccept],
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# ['Zamietnutých', countReject],
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# ['Preskočených', countSkip],
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# ['------------', '------------'],
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# ['Spolu', countSpans]
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#]
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#
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#table = [
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# ['Entita', 'Počet'],
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# ['PER', countPER],
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# ['LOC', countLOC],
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# ['ORG', countORG],
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# ['MISC', countMISC]
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#]
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#print('\nPočet anotovaných článkov:')
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#TableIt.printTable(table1)
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#print('\nPočet jednotlivých entít:')
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#TableIt.printTable(table, useFieldNames=True, color=(26, 156, 171))
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# table v2
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print(underline + '\nPočet anotovaných článkov:' + reset)
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print(green + "%-15s %-20s" %("Prijatých", countAccept) + reset)
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print(red + "%-15s %-15s" %("Zamietnutých", countReject) + reset)
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print(gray + "%-15s %-15s" %("Preskočených", countSkip) + reset)
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print("%-15s" %("---------------------"))
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print("%-15s %-15s" %("Spolu", countSpans))
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print(underline + '\nPočet jednotlivých entít:' + reset)
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print("%-10s %-10s" %("Entita:", "Počet:"))
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print("%-10s" %("----------------"))
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print("%-10s %-10s" %("PER", countPER))
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print("%-10s %-10s" %("LOC", countLOC))
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print("%-10s %-10s" %("ORG", countORG))
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print("%-10s %-10s" %("MISC", countMISC))
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# kto anotoval koľko?
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frequency = {}
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#Open the sample text file in read mode.
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#document_text = open('sample.txt', 'r')
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#convert the string of the document in lowercase and assign it to text_string variable.
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#text = document_text.read().lower()
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regex1 = '"_session_id":(.*?),'
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pattern = re.findall(regex1, text)
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for word in pattern:
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count = frequency.get(word,0)
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frequency[word] = count + 1
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frequency_list = frequency.keys()
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print(underline + '\nKto anotoval koľko článkov?' + reset)
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for words in frequency_list:
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print(words, frequency[words])
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@plac.annotations(
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annotator=("Keep annotations from this annotator (email address or nickname)", "positional", None, str),
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dataset_path=("Path to dataset (Prodigy JSONL format)", "positional", None, str),
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new_dataset_path=("Path to save new dataset(Prodigy JSONL format)", "positional", None, str),
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)
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def delete_annot(
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annotator, dataset_path, new_dataset_path
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):
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"""
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Load Prodigy JSONL dataset,
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and keep annotations only from one annotator.
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"""
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file1 = open(sys.argv[2], 'r', encoding='utf-8')
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file2 = open(sys.argv[3],'w', encoding='utf-8')
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for line in file1.readlines():
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x = re.findall(sys.argv[1], line)
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if x:
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print(line)
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file2.write(line)
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file1.close()
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file2.close()
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def modelinfo(
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):
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"""
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Print information about trained model (Precision, Recall and F-Score)
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"""
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with open('build/train/nerposparser/model-best/meta.json', 'rt') as f:
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for line in itertools.islice(f, 31, 54):
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print(line, end =" ")
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def helpme(
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):
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print("Available commands:",
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"\ncount - Print statistics about Prodigy JSONL dataset",
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"\ndelete_annot - Create dataset with annotations from only specific annotator",
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"\nmodelinfo - Prints informations about trained model (Precision, Recall and F-Score)")
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if __name__ == "__main__":
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opts = {"count": count,
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"delete_annot": delete_annot,
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"modelinfo": modelinfo,
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"help": helpme}
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cmd = sys.argv.pop(1)
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if cmd not in opts:
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msg.fail(f"Unknown command: {cmd}", f"Available: {', '.join(opts)}", exits=1)
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try:
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plac.call(opts[cmd])
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except KeyboardInterrupt:
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msg.warn("Stopped.", exits=1)
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@ -0,0 +1,24 @@
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set -e
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OUTDIR=build/train/output
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TRAINDIR=build/train
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# Delete old training results
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rm -r $TRAINDIR
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mkdir -p $TRAINDIR
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mkdir -p $OUTDIR
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mkdir -p dist
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# Delete old training results
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rm -rf $OUTDIR/*
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# Train dependency and POS
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spacy train sk $OUTDIR ./build/input/slovak-treebank ./build/input/ud-artificial-gapping --n-iter 15 -p tagger,parser
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rm -rf $TRAINDIR/posparser
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mv $OUTDIR/model-best $TRAINDIR/posparser
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# Train NER
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# custom script for training, but it takes too long... input is JSONL file (db from Prodigy)
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# python custom_train.py train ./build/train/posparser ./train.jsonl ./eval.jsonl -o ./build/train/nerposparser -n 15
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spacy train sk $TRAINDIR/nerposparser ./ner/experiments/34sknerfull.json ./ner/experiments/34wikiartfull.json --n-iter 15 -p ner
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# Package model
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spacy package $TRAINDIR/nerposparser dist --meta-path ./meta.json --force
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cd dist/sk_sk1-0.2.0
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python ./setup.py sdist --dist-dir ../
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@ -0,0 +1,80 @@
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"""
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Visualize the data with Streamlit and spaCy.
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https://github.com/explosion/projects/blob/master/ner-drugs/streamlit_visualizer.py
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Usage example:
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$ streamlit run visualizer.py visualize ./dataset.jsonl
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"""
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import streamlit as st
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from spacy import displacy
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import srsly
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import sys
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import plac
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from wasabi import msg
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@plac.annotations(
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dataset_path=("Path to dataset (Prodigy JSONL format)", "positional", None, str),
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)
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def visualize(
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dataset_path
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):
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FILES = [sys.argv[1]]
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MISC = "MISC"
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||||||
|
HTML_WRAPPER = "<div style='border-bottom: 1px solid #ccc; padding: 20px 0'>{}</div>"
|
||||||
|
HTML_WRAPPER1 = "<div style='border-bottom: 2px solid #000; padding: 0 0 20px 0'>{}</div>"
|
||||||
|
SETTINGS = {"style": "ent", "manual": True, "options": {"colors": {MISC: "#d1bcff"}}}
|
||||||
|
|
||||||
|
@st.cache(allow_output_mutation=True)
|
||||||
|
def load_data(filepath):
|
||||||
|
return list(srsly.read_jsonl(filepath))
|
||||||
|
|
||||||
|
st.sidebar.title("Data visualizer")
|
||||||
|
st.sidebar.markdown(
|
||||||
|
"Visualize the annotations using [displaCy](https://spacy.io/usage/visualizers) "
|
||||||
|
"and view stats about the datasets."
|
||||||
|
)
|
||||||
|
data_file = st.sidebar.selectbox("Dataset", FILES)
|
||||||
|
data = load_data(data_file)
|
||||||
|
n_no_ents = 0
|
||||||
|
n_total_ents = 0
|
||||||
|
accepted = 0
|
||||||
|
rejected = 0
|
||||||
|
|
||||||
|
st.header(f"Dataset: {data_file} ({len(data)})")
|
||||||
|
st.markdown(HTML_WRAPPER1.format("Visualize only accepted examples and their annotations."), unsafe_allow_html=True)
|
||||||
|
for eg in data:
|
||||||
|
if eg["answer"] == "accept":
|
||||||
|
accepted += 1
|
||||||
|
if eg["answer"] != "accept":
|
||||||
|
rejected += 1
|
||||||
|
continue
|
||||||
|
row = {"text": eg["text"], "ents": eg.get("spans", [])}
|
||||||
|
answer = {"answer": eg.get("answer", [])}
|
||||||
|
n_total_ents += len(row["ents"])
|
||||||
|
if not row["ents"]:
|
||||||
|
n_no_ents += 1
|
||||||
|
html = displacy.render(row, **SETTINGS).replace("\n\n", "\n")
|
||||||
|
st.markdown(HTML_WRAPPER.format(html), unsafe_allow_html=True)
|
||||||
|
|
||||||
|
st.sidebar.markdown(
|
||||||
|
f"""
|
||||||
|
| `{data_file}` | |
|
||||||
|
| --- | ---: |
|
||||||
|
| Total examples | {len(data):,} |
|
||||||
|
| Accepted examples | {accepted:,} |
|
||||||
|
| Rejected examples | {rejected:,} |
|
||||||
|
| Total entities | {n_total_ents:,} |
|
||||||
|
| Examples with no entities | {n_no_ents:,} |
|
||||||
|
""", unsafe_allow_html=True
|
||||||
|
)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
opts = {"visualize": visualize}
|
||||||
|
cmd = sys.argv.pop(1)
|
||||||
|
if cmd not in opts:
|
||||||
|
msg.fail(f"Unknown command: {cmd}", f"Available: {', '.join(opts)}", exits=1)
|
||||||
|
try:
|
||||||
|
plac.call(opts[cmd])
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
msg.warn("Stopped.", exits=1)
|
||||||
Loading…
Reference in New Issue
Block a user