en-ner-conll-2003/3_RNN — kopia — kopia copy.ipynb

Uczenie głębokie – przetwarzanie tekstu – laboratoria

3. RNN

%pip install --ignore-installed --force-reinstall -v torch torchtext --index-url https://download.pytorch.org/whl/cu118

^C
Note: you may need to restart the kernel to use updated packages.
Using pip 24.0 from c:\Python312\Lib\site-packages\pip (python 3.12)
Looking in indexes: https://download.pytorch.org/whl/cu118
Collecting torch
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Collecting filelock (from torch)
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Collecting numpy (from torchtext)
  Using cached https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-win_amd64.whl (15.5 MB)
Collecting torchdata==0.7.1 (from torchtext)
  Using cached https://download.pytorch.org/whl/torchdata-0.7.1-py3-none-any.whl (184 kB)
Collecting urllib3>=1.25 (from torchdata==0.7.1->torchtext)
  Using cached https://download.pytorch.org/whl/urllib3-1.26.13-py2.py3-none-any.whl (140 kB)
Collecting MarkupSafe>=2.0 (from jinja2->torch)
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Collecting charset-normalizer<3,>=2 (from requests->torchtext)
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Collecting idna<4,>=2.5 (from requests->torchtext)
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Collecting tbb==2021.* (from mkl<=2021.4.0,>=2021.1.1->torch)
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Installing collected packages: mpmath, urllib3, typing-extensions, sympy, numpy, networkx, MarkupSafe, idna, fsspec, filelock, colorama, charset-normalizer, certifi, tqdm, requests, jinja2, torch, torchdata, torchtext

ERROR: Could not install packages due to an OSError.
Consider using the `--user` option or check the permissions.
Traceback (most recent call last):
  File "c:\Python312\Lib\site-packages\pip\_internal\commands\install.py", line 452, in run
    installed = install_given_reqs(
                ^^^^^^^^^^^^^^^^^^^
  File "c:\Python312\Lib\site-packages\pip\_internal\req\__init__.py", line 72, in install_given_reqs
    requirement.install(
  File "c:\Python312\Lib\site-packages\pip\_internal\req\req_install.py", line 856, in install
    install_wheel(
  File "c:\Python312\Lib\site-packages\pip\_internal\operations\install\wheel.py", line 725, in install_wheel
    _install_wheel(
  File "c:\Python312\Lib\site-packages\pip\_internal\operations\install\wheel.py", line 585, in _install_wheel
    file.save()
  File "c:\Python312\Lib\site-packages\pip\_internal\operations\install\wheel.py", line 378, in save
    os.unlink(self.dest_path)
PermissionError: [WinError 5] Odmowa dostępu: 'c:\\\\Python312\\\\Lib\\\\site-packages\\\\numpy\\\\core\\\\_multiarray_tests.cp312-win_amd64.pyd'

from collections import Counter
import torch
from torchtext.vocab import vocab
from tqdm import tqdm
from ipywidgets import FloatProgress

import pandas as pd
from nltk.tokenize import word_tokenize
from unidecode import unidecode

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
File c:\Python312\Lib\site-packages\requests\compat.py:11
     10 try:
---> 11     import chardet
     12 except ImportError:

ModuleNotFoundError: No module named 'chardet'

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
Cell In[1], line 3
      1 from collections import Counter
      2 import torch
----> 3 from torchtext.vocab import vocab
      4 from tqdm import tqdm
      5 from ipywidgets import FloatProgress

File c:\Python312\Lib\site-packages\torchtext\__init__.py:12
      8 _TEXT_BUCKET = "https://download.pytorch.org/models/text/"
     10 _CACHE_DIR = os.path.expanduser(os.path.join(_get_torch_home(), "text"))
---> 12 from . import data, datasets, prototype, functional, models, nn, transforms, utils, vocab, experimental
     14 try:
     15     from .version import __version__, git_version  # noqa: F401

File c:\Python312\Lib\site-packages\torchtext\datasets\__init__.py:3
      1 import importlib
----> 3 from .ag_news import AG_NEWS
      4 from .amazonreviewfull import AmazonReviewFull
      5 from .amazonreviewpolarity import AmazonReviewPolarity

File c:\Python312\Lib\site-packages\torchtext\datasets\ag_news.py:5
      2 from functools import partial
      3 from typing import Union, Tuple
----> 5 from torchdata.datapipes.iter import FileOpener, IterableWrapper
      6 from torchtext._download_hooks import HttpReader
      7 from torchtext._internal.module_utils import is_module_available

File c:\Python312\Lib\site-packages\torchdata\__init__.py:9
      1 # Copyright (c) Meta Platforms, Inc. and affiliates.
      2 # All rights reserved.
      3 #
      4 # This source code is licensed under the BSD-style license found in the
      5 # LICENSE file in the root directory of this source tree.
      7 from torchdata import _extension  # noqa: F401
----> 9 from . import datapipes
     11 janitor = datapipes.utils.janitor
     13 try:

File c:\Python312\Lib\site-packages\torchdata\datapipes\__init__.py:9
      1 # Copyright (c) Meta Platforms, Inc. and affiliates.
      2 # All rights reserved.
      3 #
      4 # This source code is licensed under the BSD-style license found in the
      5 # LICENSE file in the root directory of this source tree.
      7 from torch.utils.data import DataChunk, functional_datapipe
----> 9 from . import iter, map, utils
     11 __all__ = ["DataChunk", "functional_datapipe", "iter", "map", "utils"]

File c:\Python312\Lib\site-packages\torchdata\datapipes\iter\__init__.py:54
     46 from torchdata.datapipes.iter.load.huggingface import HuggingFaceHubReaderIterDataPipe as HuggingFaceHubReader
     48 from torchdata.datapipes.iter.load.iopath import (
     49     IoPathFileListerIterDataPipe as IoPathFileLister,
     50     IoPathFileOpenerIterDataPipe as IoPathFileOpener,
     51     IoPathSaverIterDataPipe as IoPathSaver,
     52 )
---> 54 from torchdata.datapipes.iter.load.online import (
     55     GDriveReaderDataPipe as GDriveReader,
     56     HTTPReaderIterDataPipe as HttpReader,
     57     OnlineReaderIterDataPipe as OnlineReader,
     58 )
     59 from torchdata.datapipes.iter.load.s3io import (
     60     S3FileListerIterDataPipe as S3FileLister,
     61     S3FileLoaderIterDataPipe as S3FileLoader,
     62 )
     63 from torchdata.datapipes.iter.transform.bucketbatcher import (
     64     BucketBatcherIterDataPipe as BucketBatcher,
     65     InBatchShufflerIterDataPipe as InBatchShuffler,
     66     MaxTokenBucketizerIterDataPipe as MaxTokenBucketizer,
     67 )

File c:\Python312\Lib\site-packages\torchdata\datapipes\iter\load\online.py:12
      9 import warnings
     10 from typing import Any, Dict, Iterator, Optional, Tuple
---> 12 import requests
     14 from torchdata.datapipes import functional_datapipe
     15 from torchdata.datapipes.iter import IterDataPipe

File c:\Python312\Lib\site-packages\requests\__init__.py:45
     41 import warnings
     43 import urllib3
---> 45 from .exceptions import RequestsDependencyWarning
     47 try:
     48     from charset_normalizer import __version__ as charset_normalizer_version

File c:\Python312\Lib\site-packages\requests\exceptions.py:9
      1 """
      2 requests.exceptions
      3 ~~~~~~~~~~~~~~~~~~~
      4 
      5 This module contains the set of Requests' exceptions.
      6 """
      7 from urllib3.exceptions import HTTPError as BaseHTTPError
----> 9 from .compat import JSONDecodeError as CompatJSONDecodeError
     12 class RequestException(IOError):
     13     """There was an ambiguous exception that occurred while handling your
     14     request.
     15     """

File c:\Python312\Lib\site-packages\requests\compat.py:13
     11     import chardet
     12 except ImportError:
---> 13     import charset_normalizer as chardet
     15 import sys
     17 # -------
     18 # Pythons
     19 # -------
     20 
     21 # Syntax sugar.

File c:\Python312\Lib\site-packages\charset_normalizer\__init__.py:24
      2 """
      3 Charset-Normalizer
      4 ~~~~~~~~~~~~~~
   (...)
     20 :license: MIT, see LICENSE for more details.
     21 """
     22 import logging
---> 24 from .api import from_bytes, from_fp, from_path, normalize
     25 from .legacy import (
     26     CharsetDetector,
     27     CharsetDoctor,
   (...)
     30     detect,
     31 )
     32 from .models import CharsetMatch, CharsetMatches

File c:\Python312\Lib\site-packages\charset_normalizer\api.py:7
      4 from os.path import basename, splitext
      5 from typing import Any, BinaryIO, List, Optional, Set
----> 7 from .cd import (
      8     coherence_ratio,
      9     encoding_languages,
     10     mb_encoding_languages,
     11     merge_coherence_ratios,
     12 )
     13 from .constant import IANA_SUPPORTED, TOO_BIG_SEQUENCE, TOO_SMALL_SEQUENCE, TRACE
     14 from .md import mess_ratio

File c:\Python312\Lib\site-packages\charset_normalizer\cd.py:9
      7 from .assets import FREQUENCIES
      8 from .constant import KO_NAMES, LANGUAGE_SUPPORTED_COUNT, TOO_SMALL_SEQUENCE, ZH_NAMES
----> 9 from .md import is_suspiciously_successive_range
     10 from .models import CoherenceMatches
     11 from .utils import (
     12     is_accentuated,
     13     is_latin,
   (...)
     16     unicode_range,
     17 )

AttributeError: partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)

torch.cuda.is_available()

True

# odczytaj dane treningowe
train = pd.read_csv('train/train.tsv', sep='\t')
train.columns = ["y", "x"]
print("wczytano dane treningowe")
print(train["y"][0], train["x"][0])

# podziel dane treningowe na słowa
# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
slowa_train = []
for tekst in train["x"]:
    pom = []
    for slowo in tekst.split(" "):
        #if slowo not in ("<","/s",">","/S","``"):
        pom.append(slowo.lower())
    slowa_train.append(pom)
print("podzielono dane treningowe na słowa")
print(slowa_train[0])

wczytano dane treningowe
O B-PER O O O O O O O O O B-LOC O O O O O O O O O O O B-LOC O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O B-LOC O O O O O O O O O O O O O B-MISC I-MISC I-MISC I-MISC O O O B-PER O O O O O B-LOC O O O O O O O O O O O O O O O O O B-MISC O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O B-PER O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O O O O O O Rare Hendrix song draft sells for almost $ 17,000 . </S> LONDON 1996-08-22 </S> A rare early handwritten draft of a song by U.S. guitar legend Jimi Hendrix was sold for almost $ 17,000 on Thursday at an auction of some of the late musician 's favourite possessions . </S> A Florida restaurant paid 10,925 pounds ( $ 16,935 ) for the draft of " Ai n't no telling " , which Hendrix penned on a piece of London hotel stationery in late 1966 . </S> At the end of a January 1967 concert in the English city of Nottingham he threw the sheet of paper into the audience , where it was retrieved by a fan . </S> Buyers also snapped up 16 other items that were put up for auction by Hendrix 's former girlfriend Kathy Etchingham , who lived with him from 1966 to 1969 . </S> They included a black lacquer and mother of pearl inlaid box used by Hendrix to store his drugs , which an anonymous Australian purchaser bought for 5,060 pounds ( $ 7,845 ) . </S> The guitarist died of a drugs overdose in 1970 aged 27 . </S>
podzielono dane treningowe na słowa
['rare', 'hendrix', 'song', 'draft', 'sells', 'for', 'almost', '$', '17,000', '.', '</s>', 'london', '1996-08-22', '</s>', 'a', 'rare', 'early', 'handwritten', 'draft', 'of', 'a', 'song', 'by', 'u.s.', 'guitar', 'legend', 'jimi', 'hendrix', 'was', 'sold', 'for', 'almost', '$', '17,000', 'on', 'thursday', 'at', 'an', 'auction', 'of', 'some', 'of', 'the', 'late', 'musician', "'s", 'favourite', 'possessions', '.', '</s>', 'a', 'florida', 'restaurant', 'paid', '10,925', 'pounds', '(', '$', '16,935', ')', 'for', 'the', 'draft', 'of', '"', 'ai', "n't", 'no', 'telling', '"', ',', 'which', 'hendrix', 'penned', 'on', 'a', 'piece', 'of', 'london', 'hotel', 'stationery', 'in', 'late', '1966', '.', '</s>', 'at', 'the', 'end', 'of', 'a', 'january', '1967', 'concert', 'in', 'the', 'english', 'city', 'of', 'nottingham', 'he', 'threw', 'the', 'sheet', 'of', 'paper', 'into', 'the', 'audience', ',', 'where', 'it', 'was', 'retrieved', 'by', 'a', 'fan', '.', '</s>', 'buyers', 'also', 'snapped', 'up', '16', 'other', 'items', 'that', 'were', 'put', 'up', 'for', 'auction', 'by', 'hendrix', "'s", 'former', 'girlfriend', 'kathy', 'etchingham', ',', 'who', 'lived', 'with', 'him', 'from', '1966', 'to', '1969', '.', '</s>', 'they', 'included', 'a', 'black', 'lacquer', 'and', 'mother', 'of', 'pearl', 'inlaid', 'box', 'used', 'by', 'hendrix', 'to', 'store', 'his', 'drugs', ',', 'which', 'an', 'anonymous', 'australian', 'purchaser', 'bought', 'for', '5,060', 'pounds', '(', '$', '7,845', ')', '.', '</s>', 'the', 'guitarist', 'died', 'of', 'a', 'drugs', 'overdose', 'in', '1970', 'aged', '27', '.', '</s>']

# odczytaj dane testowe dev-0
test_dev0 = pd.read_csv('dev-0/in.tsv', sep='\t')
test_dev0.columns = ["x"]
print("wczytano dane testowe dev-0")
print(test_dev0["x"][0])

# podziel dane testowe na słowa
# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
slowa_test_dev0 = []
for tekst in test_dev0["x"]:
    pom = []
    for slowo in tekst.split(" "):
        #if slowo not in ("<","/s",">","/S","``"):
        pom.append(slowo.lower())
    slowa_test_dev0.append(pom)
print("podzielono dane treningowe na słowa")
print(slowa_test_dev0[0])

wczytano dane testowe dev-0
CRICKET - ENGLISH COUNTY CHAMPIONSHIP SCORES . </S> LONDON 1996-08-30 </S> Result and close of play scores in English county championship matches on Friday : </S> Leicester : Leicestershire beat Somerset by an innings and 39 runs . </S> Somerset 83 and 174 ( P. Simmons 4-38 ) , Leicestershire 296 . </S> Leicestershire 22 points , Somerset 4 . </S> Chester-le-Street : Glamorgan 259 and 207 ( A. Dale 69 , H. Morris 69 ; D. Blenkiron 4-43 ) , Durham 114 ( S. Watkin 4-28 ) and 81-3 . </S> Tunbridge Wells : Nottinghamshire 214 ( P. Johnson 84 ; M. McCague 4-55 ) , Kent 108-3 . </S> London ( The Oval ) : Warwickshire 195 , Surrey 429-7 ( C. Lewis 80 not out , M. Butcher 70 , G. Kersey 63 , J. Ratcliffe 63 , D. Bicknell 55 ) . </S> Hove : Sussex 363 ( W. Athey 111 , V. Drakes 52 ; I. Austin 4-37 ) , Lancashire 197-8 ( W. Hegg 54 ) </S> Portsmouth : Middlesex 199 and 426 ( J. Pooley 111 , M. Ramprakash 108 , M. Gatting 83 ) , Hampshire 232 and 109-5 . </S> Chesterfield : Worcestershire 238 and 133-5 , Derbyshire 471 ( J. Adams 123 , T.O'Gorman 109 not out , K. Barnett 87 ; T. Moody 6-82 ) </S> Bristol : Gloucestershire 183 and 185-6 ( J. Russell 56 not out ) , Northamptonshire 190 ( K. Curran 52 ; A. Smith 5-68 ) . </S>
podzielono dane treningowe na słowa
['cricket', '-', 'english', 'county', 'championship', 'scores', '.', '</s>', 'london', '1996-08-30', '</s>', 'result', 'and', 'close', 'of', 'play', 'scores', 'in', 'english', 'county', 'championship', 'matches', 'on', 'friday', ':', '</s>', 'leicester', ':', 'leicestershire', 'beat', 'somerset', 'by', 'an', 'innings', 'and', '39', 'runs', '.', '</s>', 'somerset', '83', 'and', '174', '(', 'p.', 'simmons', '4-38', ')', ',', 'leicestershire', '296', '.', '</s>', 'leicestershire', '22', 'points', ',', 'somerset', '4', '.', '</s>', 'chester-le-street', ':', 'glamorgan', '259', 'and', '207', '(', 'a.', 'dale', '69', ',', 'h.', 'morris', '69', ';', 'd.', 'blenkiron', '4-43', ')', ',', 'durham', '114', '(', 's.', 'watkin', '4-28', ')', 'and', '81-3', '.', '</s>', 'tunbridge', 'wells', ':', 'nottinghamshire', '214', '(', 'p.', 'johnson', '84', ';', 'm.', 'mccague', '4-55', ')', ',', 'kent', '108-3', '.', '</s>', 'london', '(', 'the', 'oval', ')', ':', 'warwickshire', '195', ',', 'surrey', '429-7', '(', 'c.', 'lewis', '80', 'not', 'out', ',', 'm.', 'butcher', '70', ',', 'g.', 'kersey', '63', ',', 'j.', 'ratcliffe', '63', ',', 'd.', 'bicknell', '55', ')', '.', '</s>', 'hove', ':', 'sussex', '363', '(', 'w.', 'athey', '111', ',', 'v.', 'drakes', '52', ';', 'i.', 'austin', '4-37', ')', ',', 'lancashire', '197-8', '(', 'w.', 'hegg', '54', ')', '</s>', 'portsmouth', ':', 'middlesex', '199', 'and', '426', '(', 'j.', 'pooley', '111', ',', 'm.', 'ramprakash', '108', ',', 'm.', 'gatting', '83', ')', ',', 'hampshire', '232', 'and', '109-5', '.', '</s>', 'chesterfield', ':', 'worcestershire', '238', 'and', '133-5', ',', 'derbyshire', '471', '(', 'j.', 'adams', '123', ',', "t.o'gorman", '109', 'not', 'out', ',', 'k.', 'barnett', '87', ';', 't.', 'moody', '6-82', ')', '</s>', 'bristol', ':', 'gloucestershire', '183', 'and', '185-6', '(', 'j.', 'russell', '56', 'not', 'out', ')', ',', 'northamptonshire', '190', '(', 'k.', 'curran', '52', ';', 'a.', 'smith', '5-68', ')', '.', '</s>']

# odczytaj dane testowe A
test_A = pd.read_csv('test-A/in.tsv', sep='\t')
test_A.columns = ["x"]
print("wczytano dane testowe A")
print(test_A["x"][0])

# podziel dane testowe na słowa
# https://www.geeksforgeeks.org/python-word-embedding-using-word2vec/
slowa_test_A = []
for tekst in test_A["x"]:
    pom = []
    for slowo in tekst.split(" "):
        #if slowo not in ("<","/s",">","/S","``"):
        pom.append(slowo.lower())
    slowa_test_A.append(pom)
print("podzielono dane treningowe na słowa")
print(slowa_test_A[0])

wczytano dane testowe A
RUGBY UNION - CUTTITTA BACK FOR ITALY AFTER A YEAR . </S> ROME 1996-12-06 </S> Italy recalled Marcello Cuttitta </S> on Friday for their friendly against Scotland at Murrayfield more than a year after the 30-year-old wing announced he was retiring following differences over selection . </S> Cuttitta , who trainer George Coste said was certain to play on Saturday week , was named in a 21-man squad lacking only two of the team beaten 54-21 by England at Twickenham last month . </S> Stefano Bordon is out through illness and Coste said he had dropped back row Corrado Covi , who had been recalled for the England game after five years out of the national team . </S> Cuttitta announced his retirement after the 1995 World Cup , where he took issue with being dropped from the Italy side that faced England in the pool stages . </S> Coste said he had approached the player two months ago about a comeback . </S> " He ended the World Cup on the wrong note , " Coste said . </S> " I thought it would be useful to have him back and he said he would be available . </S> I think now is the right time for him to return . " </S> Squad : Javier Pertile , Paolo Vaccari , Marcello Cuttitta , Ivan Francescato , Leandro Manteri , Diego Dominguez , Francesco Mazzariol , Alessandro Troncon , Orazio Arancio , Andrea Sgorlon , Massimo Giovanelli , Carlo Checchinato , Walter Cristofoletto , Franco Properzi Curti , Carlo Orlandi , Massimo Cuttitta , Giambatista Croci , Gianluca Guidi , Nicola Mazzucato , Alessandro Moscardi , Andrea Castellani . </S>
podzielono dane treningowe na słowa
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def build_vocab(dataset):
    counter = Counter()
    for document in dataset:
        counter.update(document)
    return vocab(counter, specials=["<unk>", "<pad>", "<bos>", "<eos>"])

v = build_vocab(slowa_train)
v.set_default_index(v["<unk>"])
itos = v.get_itos()  # mapowanie indeksów na tokeny
print(len(itos))  # liczba różnych tokenów w słowniku
print(itos)

20998
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'meadow', 'hindered', 'excavations', 'jumet', 'sex-and-murder', 'lightly', 'tides', 'pesetas', 'chemicals', '4,150', '61.45', '61.94', '4,175', 'madness', 'overreacted', '+34', '585', '2161', 'nahar', 'liberte', 'leveilly', 'finns', 'sex-abuse', 'captive', 'tampere', 'luxury', 'ilkka', 'laasonen', 'iltalehti', 'audi', 'ingolstadt', 'demel', 'hedged', 'kekkila', 'full-yr', 'fertilisers', 'saplings', 'trend', 'fluctuations', 'end-year', 'interview-t&n', 'untroubled', 't&n', 'bumble', 'predict', 'echoing', 'automotive', 'equally', 'predicting', 'rationalised', 'destocked', '9.5', 'destocking', 'relaxed', 'cycle', 'fraction', 'commenting', 'piston', 'kolbenschmidt', 'obstacles', 'huddart', '8716', 'jolts', 'zealands', 'waiau', 'spa', 'hanmer', 'prone', 'earthquakes', 'smooth', 'trevelyan', 'half-yearly', 'absolutely', 'predicated', 'precepts', 'trough', 'draconian', 'rentals', 'reclamation', 'narrowly', 'downtrend', 'hover', 't$', '27.482', '27.495', '275', '2-5080815', '20,500', '20,605', 'touching', 'intraday', '20,530', '20,725', '19,560', 'technically', '20,300', '21,000', 'doreen', 'siow', '65-8703092', 'siam', 'banker', '8.46', '662', '652-0642', "m'bishi", '7-year', 'tokyo-mitsubishi', '2.95', 'sep.03', 'sep.96', 'int', 'mar.97', 'sub', 'jul-18.jul', 'jcr', 'jbri', 'daewoo', 'dacom', '822', '727', '5644', 'commem', 'manufactures', 'ski', 'antara', 'evident', 'happening', 'motivated', 'major-general', 'utomo', 'indonesias', 'extinguished', 'injures', 'exaggerate', '11.38', '11.45', '918', '490', '538', 'rents', 'haneda', 'itami', 'beijing-backed', 'post-handover', 'contesting', 'stymies', '400-strong', 'dismantle', 'fully-elected', 'considerable', 'directorate-grade', 'bureaucrats', '33,000', 'tier', 'mandarin', 'secretaries', 'src', 'barrel-per-day', 'distillation', 'stuck', 'x-ray', 'no.3', 'varied', 'exchanger', '285,000', 'co-owned', 'petrolem', 'joint-venture', 'chevron', 'texaco', '+65-8703086', 'loxley', '332.66', 'reviewed', '6.66', '266.37', '662-252-9950', 'mth', 'pvs', 'yr-ago', '+0.1', '+0.0', '119.3**', '+3.7', '+3.6', '+4.7', '119.3', '+0,2', '+3.5', '+5.2', 'jobless', 'inem', '63,913', '33,149', '65,345', '2.17m', '13.67', '14.15', '15.19', '196.8', '180.6', '279.9', 'cur', '110.4', '+1,161', '+400.9', '+310.4', '54,703.0', '+0.2', '119.6**', '+1.2', '+1.5', '+7.1', '119.6', 'prod', '+1.0', '+9.8', '108.4**', 'm4', '+2.6', '+4.2r', '+10.8', 'adj', 'trln', '75.912', '1,100.7', '1,164.1', '988.2', '1,315.7', '1,433.4', '1,236.5', '215.0', '269.3', '248.3', '1,334.0', '1,119.0', '1,420.9', 'govt.budget', 'govt.fcast', '+282.1', '380.6', '+230.4', 'def', '1,184.0', '1,466.1', '1,456.7', 'qtr', 'epa', '+168,130', '+31,230', '+167,330', 'yr-yr', '+1.9', '+2.3r', '+3.4', '18.1', '16.9', '69.7', '7.25', '7.50', '04/06/96', 'customs-cleared', 'annualised', 'ine', 'variation', 'total-', 'corresponds', 'relate', 'finances', '**general', '100=1992', '100=1990', 'obstructing', 'lasa-zabala', 'gal', 'prescriptions', 'gomez', 'liano', 'aznar', 'bch', 'hive', 'coopers', 'lybrand', 'emigrates', 'lenzing', 'viscose', 'fibre', 'preview', '84.5', 'schillings', 'fibres', 'economies', '+431', '53112', 'fundamentalists', 'masterminds', 'ideologists', 'financers', 'clarifications', 'clarification', '1676/96', 'amending', '2454/93', '2913/92', 'donna', 'topic', 'pundits', 'debated', 'forgo', 'unflagging', 'sidestepped', 'strong-dollar', 'bentsen', 'viewed', 'distinctively', 'widness', 'timely', 'instances', 'post-second', '1.3438', '79.75', 'stands', '1.48', 'expertise', 'co-chairman', 'faust', 'bailard', 'biehl', 'kaiser', 'awfully', 'bentsen-era', 'greenspan', 'reappointment', 'perelstein', 'mainstay', 'amstetten', 'ewald', 'brenner', 'stieglmair', 'brunmayr', '43rd', 'marino', 'hartson', 'best-of-three', 'ex-world', '24-kms', 'samokhalova', 'spirited', 'quigley', 'numerical', 'superiority', 'goulnara', 'fatkoullina', 'snatching', 'lapped', 'ingrid', 'haringa', 'lancien', '12.341', '12.348', '12.130', '12.124', '12.112', '12.246', '11.959', '12.225', '24-km', '31.081', 'tatiana', 'stiajkina', 'arndt', 'vikstedt-nyman', 'boyden', 'godras', 'kombouare', 'abderdeen', '467,000', 'aitken', 'affection', 'ginola', 'influential', 'influenced', 'imola', 'doohan', '50.250', 'bayle', '1:50.727', 'norifumi', 'abe', '1:50.858', 'luca', 'cadalora', '1:51.006', 'criville', '1:51.075', '1:51.287', 'tadayuki', 'okada', '1:51.528', 'checa', '1:51.588', '1:51.784', 'shinichi', 'itoh', '1:51.857', '216', '217', 'swe', 'bradley', 'hughes']

# slownik etykiety - kody etykiet
etykieta_na_kod = {}
licznik = 0
for tekst in train["y"]:
    for etykieta in tekst.split(" "):
        if etykieta not in etykieta_na_kod:
            etykieta_na_kod[etykieta] = licznik
            licznik+=1
print(etykieta_na_kod)

{'O': 0, 'B-PER': 1, 'B-LOC': 2, 'I-PER': 3, 'B-MISC': 4, 'I-MISC': 5, 'I-LOC': 6, 'B-ORG': 7, 'I-ORG': 8}

# podziel etykiety
kody_etykiet_train = []
for tekst in train["y"]:
    pom = []
    for etykieta in tekst.split(" "):
        pom.append(etykieta_na_kod[etykieta])
    kody_etykiet_train.append(pom)
print(kody_etykiet_train[0])

[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 5, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

# odczytaj etykiety dev-0
labels_dev0 = pd.read_csv('dev-0/expected.tsv', sep='\t')
labels_dev0.columns = ["y"]
print(labels_dev0["y"][0])

# podziel etykiety
kody_etykiet_dev0 = []
for tekst in labels_dev0["y"]:
    pom = []
    for etykieta in tekst.split(" "):
        pom.append(etykieta_na_kod[etykieta])
    kody_etykiet_dev0.append(pom)
print(kody_etykiet_dev0[0])

O O B-MISC I-MISC I-MISC O O O B-LOC O O O O O O O O O B-MISC O O O O O O O B-LOC O B-ORG O B-ORG O O O O O O O O B-ORG O O O O B-PER I-PER O O O B-ORG O O O B-ORG O O O B-ORG O O O B-LOC O B-ORG O O O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O B-ORG O O B-PER I-PER O O O O O O B-LOC I-LOC O B-ORG O O B-PER I-PER O O B-PER I-PER O O O B-ORG O O O B-LOC O B-LOC I-LOC O O B-ORG O O B-ORG O O B-PER I-PER O O O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O O B-LOC O B-ORG O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O B-ORG O O B-PER I-PER O O O B-LOC O B-ORG O O O O B-PER I-PER O O B-PER I-PER O O B-PER I-PER O O O B-ORG O O O O O B-LOC O B-ORG O O O O B-ORG O O B-PER I-PER O O B-PER O O O O B-PER I-PER O O B-PER I-PER O O O B-LOC O B-ORG O O O O B-PER I-PER O O O O O B-ORG O O B-PER I-PER O O B-PER I-PER O O O O
[0, 0, 4, 5, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 2, 6, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 2, 0, 2, 6, 0, 0, 7, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 2, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0]

def data_process(dt):
    # Wektoryzacja dokumentów tekstowych.
    return [
        torch.tensor(
            [v["<bos>"]] + [v[token] for token in document] + [v["<eos>"]],
            dtype=torch.long,
        )
        for document in dt
    ]

def labels_process(dt):
    # Wektoryzacja etykiet (NER)
    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]

train_tokens_ids = data_process(slowa_train)
test_dev0_tokens_ids = data_process(slowa_test_dev0)
test_A_tokens_ids = data_process(slowa_test_A)

train_labels = labels_process(kody_etykiet_train)
test_dev0_labels = labels_process(kody_etykiet_dev0)

print(len(train_tokens_ids), len(train_tokens_ids[0]))
print(len(test_dev0_tokens_ids), len(test_dev0_tokens_ids[0]))
print(len(test_A_tokens_ids), len(test_A_tokens_ids[0]))

print(train_tokens_ids[0])
print(test_dev0_tokens_ids[0])
print(test_A_tokens_ids[0])

944 199
214 256
229 283
tensor([  2,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,
         14,  17,   4,  18,  19,   7,  20,  17,   6,  21,  22,  23,  24,  25,
          5,  26,  27,   9,  10,  11,  12,  28,  29,  30,  31,  32,  20,  33,
         20,  34,  35,  36,  37,  38,  39,  13,  14,  17,  40,  41,  42,  43,
         44,  45,  11,  46,  47,   9,  34,   7,  20,  48,  49,  50,  51,  52,
         48,  53,  54,   5,  55,  28,  17,  56,  20,  15,  57,  58,  59,  35,
         60,  13,  14,  30,  34,  61,  20,  17,  62,  63,  64,  59,  34,  65,
         66,  20,  67,  68,  69,  34,  70,  20,  71,  72,  34,  73,  53,  74,
         75,  26,  76,  21,  17,  77,  13,  14,  78,  79,  80,  81,  82,  83,
         84,  85,  86,  87,  81,   9,  32,  21,   5,  37,  88,  89,  90,  91,
         53,  92,  93,  94,  95,  96,  60,  97,  98,  13,  14,  99, 100,  17,
        101, 102, 103, 104,  20, 105, 106, 107, 108,  21,   5,  97, 109, 110,
        111,  53,  54,  31, 112, 113, 114, 115,   9, 116,  44,  45,  11, 117,
         47,  13,  14,  34, 118, 119,  20,  17, 111, 120,  59, 121, 122, 123,
         13,  14,   3])
tensor([    2,  1949,   459,    65,  1950,  1951,  1592,    13,    14,    15,
        19342,    14,  1793,   103,  1465,    20,  1952,  1592,    59,    65,
         1950,  1951,  1954,    28,   947,   166,    14,  1992,   166,  1993,
         1703,  1965,    21,    31,  2038,   103,  3671,  2932,    13,    14,
         1965,  6226,   103, 16331,    45,  1995,  1996,     0,    47,    53,
         1993,     0,    13,    14,  1993,  1055,  1330,    53,  1965,  1864,
           13,    14, 17021,   166,  1991, 19322,   103, 14088,    45,  1977,
            0,  1620,    53, 10801, 12466,  1620,  1962,  1958,     0,     0,
           47,    53,  1956, 19326,    45,  1960, 19327, 19328,    47,   103,
        16667,    13,    14, 19313,  1363,   166,  2012,     0,    45,  1995,
         2752,  5725,  1962,  1967,     0,     0,    47,    53,  1985,     0,
           13,    14,    15,    45,    34,  2037,    47,   166,  2020, 14779,
           53,  2018,     0,    45,  2030,  2059,  5455,   620,   618,    53,
         1967,     0,  1602,    53,  1963,     0,  1976,    53,  1974,     0,
         1976,    53,  1958,     0,  3843,    47,    13,    14, 19318,   166,
         2002, 16329,    45,  2024, 19320,  9379,    53,  2007,  2008,  1979,
         1962,  2061, 10865,     0,    47,    53,  2034,     0,    45,  2024,
            0,  2054,    47,    14,  6206,   166, 12584, 11568,   103, 11269,
           45,  1974, 19334,  9379,    53,  1967, 19335,  1997,    53,  1967,
        17052,  6226,    47,    53,  2000, 15584,   103,     0,    13,    14,
         9493,   166,  2026, 10970,   103,     0,    53,  9314,     0,    45,
         1974,  2717,     0,    53,     0,  6237,   620,   618,    53,  6223,
        19332, 11058,  1962,  6227,   401,     0,    47,    14,  9488,   166,
         1972, 19340,   103,     0,    45,  1974,  1975,  4451,   620,   618,
           47,    53,  2010, 14739,    45,  6223,  6224,  1979,  1962,  1977,
         4839,  1981,    47,    13,    14,     3])
tensor([    2,  6342,   769,   459,     0,   960,     9,  1681,   150,    17,
          253,    13,    14,  5474,     0,    14,  1681,  3063,     0,     0,
           14,    28,   947,     9,   701,  7189,   572,  2124,    30,     0,
          300,   301,    17,   253,   150,    34, 14863,  6363,   371,    68,
           26,  3333,   370,  3631,   608, 11618,    13,    14,     0,    53,
           92,  1738,  1753,     0,   154,    26,  3388,    97,  1952,    28,
         3978,   145,    53,    26,  2116,    59,    17,     0,  2099, 14403,
          148,   186,    20,    34,   695,  2519,     0,    21,  1208,    30,
            0,   324,   729,    13,    14,  2725,     0,   185,   618,   863,
         1521,   103,     0,   154,    68,   197,  2954,   960,  2955,     0,
            0,    53,    92,   197,   170,  3063,     9,    34,  1208,  2154,
          150,  1824,  1053,   618,    20,    34,   457,   695,    13,    14,
            0,   371,   110, 12530,   150,    34,   274,  1593,  1711,    53,
           74,    68,   596,   452,    94,  1458,  2954,    96,    34,  1681,
          749,    85,  2517,  1208,    59,    34,  8797,  9174,    13,    14,
            0,   154,    68,   197,  4705,    34,  6392,   186,   836,  2521,
          700,    17, 17097,    13,    14,    48,    68,  1240,    34,  1593,
         1711,    28,    34,  4645,  3370,    53,    48,     0,   154,    13,
           14,    48,  1500,  1798,    75,   693,   226,  5612,    97,   606,
           95,   960,   103,    68,   154,    68,   693,   226,  1221,    13,
           14,  1500,  4604,   184,   185,    34,   156,   155,     9,    95,
           97,   671,    13,    48,    14,  2099,   166,  2718,     0,    53,
            0, 16807,    53,     0,     0,    53,  2886,     0,    53,     0,
            0,    53,  2854,     0,    53, 10959,     0,    53, 11542,     0,
           53,     0,     0,    53,  2219,     0,    53,     0,     0,    53,
         4036,     0,    53, 17118,     0,    53, 11460,     0,     0,    53,
         4036,     0,    53,     0,     0,    53,     0,     0,    53,  9462,
            0,    53, 13541,     0,    53, 11542,     0,    53,  2219,     0,
           13,    14,     3])

print(len(train_labels), len(train_labels[0]))
print(len(test_dev0_labels), len(test_dev0_labels[0]))

print(train_labels[0])
print(test_dev0_labels[0])

944 199
214 256
tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        2, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 5, 5, 0, 0,
        0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 3, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
        0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0])
tensor([0, 0, 0, 4, 5, 5, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0,
        0, 0, 0, 2, 0, 7, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 1, 3, 0,
        0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0,
        0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 2, 6, 0,
        7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0, 0, 0, 2, 0, 2, 6, 0, 0, 7, 0,
        0, 7, 0, 0, 1, 3, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3,
        0, 0, 0, 0, 2, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 7, 0,
        0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 1, 3, 0,
        0, 0, 7, 0, 0, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 0,
        0, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 2, 0, 7, 0, 0, 0, 0, 1, 3, 0, 0, 0,
        0, 0, 7, 0, 0, 1, 3, 0, 0, 1, 3, 0, 0, 0, 0, 0])

def get_scores(y_true, y_pred):
    # Funkcja zwraca precyzję, pokrycie i F1
    acc_score = 0
    tp = 0
    fp = 0
    selected_items = 0
    relevant_items = 0

    for p, t in zip(y_pred, y_true):
        if p == t:
            acc_score += 1

        if p > 0 and p == t:
            tp += 1

        if p > 0:
            selected_items += 1

        if t > 0:
            relevant_items += 1

    if selected_items == 0:
        precision = 1.0
    else:
        precision = tp / selected_items

    if relevant_items == 0:
        recall = 1.0
    else:
        recall = tp / relevant_items

    if precision + recall == 0.0:
        f1 = 0.0
    else:
        f1 = 2 * precision * recall / (precision + recall)

    return precision, recall, f1

num_tags = len(etykieta_na_kod.keys())

class LSTM(torch.nn.Module):

    def __init__(self):
        super(LSTM, self).__init__()
        self.emb = torch.nn.Embedding(len(v.get_itos()), 100)
        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)
        self.fc1 = torch.nn.Linear(256, num_tags)

    def forward(self, x):
        emb = torch.relu(self.emb(x))
        lstm_output, (h_n, c_n) = self.rec(emb)
        out_weights = self.fc1(lstm_output)
        return out_weights

def eval_model(dataset_tokens, dataset_labels, model):
    Y_true = []
    Y_pred = []
    for i in tqdm(range(len(dataset_labels))):
        batch_tokens = dataset_tokens[i].unsqueeze(0)
        tags = list(dataset_labels[i].numpy())
        Y_true += tags

        Y_batch_pred_weights = model(batch_tokens).squeeze(0)
        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)
        Y_pred += list(Y_batch_pred.numpy())

    return get_scores(Y_true, Y_pred)

lstm = LSTM()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(lstm.parameters())
NUM_EPOCHS = 5

for i in range(NUM_EPOCHS):
    lstm.train()
    # for i in tqdm(range(500)):
    for i in tqdm(range(len(train_labels))):
        batch_tokens = train_tokens_ids[i].unsqueeze(0)
        tags = train_labels[i].unsqueeze(1)

        predicted_tags = lstm(batch_tokens)

        optimizer.zero_grad()
        loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))

        loss.backward()
        optimizer.step()

    lstm.eval()

 88%|████████▊ | 832/944 [00:37<00:05, 21.94it/s]

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[23], line 13
     10     optimizer.zero_grad()
     11     loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))
---> 13     loss.backward()
     14     optimizer.step()
     16 lstm.eval()

File c:\Users\Dominik\Desktop\Studia\11.sem1\en-ner-conll-2003\.venv\Lib\site-packages\torch\_tensor.py:525, in Tensor.backward(self, gradient, retain_graph, create_graph, inputs)
    515 if has_torch_function_unary(self):
    516     return handle_torch_function(
    517         Tensor.backward,
    518         (self,),
   (...)
    523         inputs=inputs,
    524     )
--> 525 torch.autograd.backward(
    526     self, gradient, retain_graph, create_graph, inputs=inputs
    527 )

File c:\Users\Dominik\Desktop\Studia\11.sem1\en-ner-conll-2003\.venv\Lib\site-packages\torch\autograd\__init__.py:267, in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    262     retain_graph = create_graph
    264 # The reason we repeat the same comment below is that
    265 # some Python versions print out the first line of a multi-line function
    266 # calls in the traceback and some print out the last line
--> 267 _engine_run_backward(
    268     tensors,
    269     grad_tensors_,
    270     retain_graph,
    271     create_graph,
    272     inputs,
    273     allow_unreachable=True,
    274     accumulate_grad=True,
    275 )

File c:\Users\Dominik\Desktop\Studia\11.sem1\en-ner-conll-2003\.venv\Lib\site-packages\torch\autograd\graph.py:744, in _engine_run_backward(t_outputs, *args, **kwargs)
    742     unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)
    743 try:
--> 744     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    745         t_outputs, *args, **kwargs
    746     )  # Calls into the C++ engine to run the backward pass
    747 finally:
    748     if attach_logging_hooks:

KeyboardInterrupt: 

print(eval_model(test_dev0_tokens_ids, test_dev0_labels, lstm))

100%|██████████| 214/214 [00:00<00:00, 262.62it/s]

(0.6558005752636625, 0.7225352112676057, 0.687552353828112)