2020-10-23 21:58:19 +08:00
|
|
|
import unittest
|
|
|
|
|
2020-11-04 06:21:04 +08:00
|
|
|
from transformers import AutoTokenizer, pipeline
|
2020-11-10 20:29:34 +08:00
|
|
|
from transformers.pipelines import Pipeline, TokenClassificationArgumentHandler
|
2020-11-04 06:21:04 +08:00
|
|
|
from transformers.testing_utils import require_tf, require_torch
|
2020-10-23 21:58:19 +08:00
|
|
|
|
|
|
|
from .test_pipelines_common import CustomInputPipelineCommonMixin
|
|
|
|
|
|
|
|
|
|
|
|
VALID_INPUTS = ["A simple string", ["list of strings"]]
|
|
|
|
|
|
|
|
|
|
|
|
class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
|
|
|
|
pipeline_task = "ner"
|
|
|
|
small_models = [
|
|
|
|
"sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
|
|
|
|
] # Default model - Models tested without the @slow decorator
|
|
|
|
large_models = [] # Models tested with the @slow decorator
|
|
|
|
|
|
|
|
def _test_pipeline(self, nlp: Pipeline):
|
|
|
|
output_keys = {"entity", "word", "score"}
|
2020-11-04 06:21:04 +08:00
|
|
|
if nlp.grouped_entities:
|
|
|
|
output_keys = {"entity_group", "word", "score"}
|
2020-10-23 21:58:19 +08:00
|
|
|
|
|
|
|
ungrouped_ner_inputs = [
|
|
|
|
[
|
2020-11-04 06:21:04 +08:00
|
|
|
{"entity": "B-PER", "index": 1, "score": 0.9994944930076599, "is_subword": False, "word": "Cons"},
|
|
|
|
{"entity": "B-PER", "index": 2, "score": 0.8025449514389038, "is_subword": True, "word": "##uelo"},
|
|
|
|
{"entity": "I-PER", "index": 3, "score": 0.9993102550506592, "is_subword": False, "word": "Ara"},
|
|
|
|
{"entity": "I-PER", "index": 4, "score": 0.9993743896484375, "is_subword": True, "word": "##új"},
|
|
|
|
{"entity": "I-PER", "index": 5, "score": 0.9992871880531311, "is_subword": True, "word": "##o"},
|
|
|
|
{"entity": "I-PER", "index": 6, "score": 0.9993029236793518, "is_subword": False, "word": "No"},
|
|
|
|
{"entity": "I-PER", "index": 7, "score": 0.9981776475906372, "is_subword": True, "word": "##guera"},
|
|
|
|
{"entity": "B-PER", "index": 15, "score": 0.9998136162757874, "is_subword": False, "word": "Andrés"},
|
|
|
|
{"entity": "I-PER", "index": 16, "score": 0.999740719795227, "is_subword": False, "word": "Pas"},
|
|
|
|
{"entity": "I-PER", "index": 17, "score": 0.9997414350509644, "is_subword": True, "word": "##tran"},
|
|
|
|
{"entity": "I-PER", "index": 18, "score": 0.9996136426925659, "is_subword": True, "word": "##a"},
|
|
|
|
{"entity": "B-ORG", "index": 28, "score": 0.9989739060401917, "is_subword": False, "word": "Far"},
|
|
|
|
{"entity": "I-ORG", "index": 29, "score": 0.7188422083854675, "is_subword": True, "word": "##c"},
|
2020-10-23 21:58:19 +08:00
|
|
|
],
|
|
|
|
[
|
2020-11-04 06:21:04 +08:00
|
|
|
{"entity": "I-PER", "index": 1, "score": 0.9968166351318359, "is_subword": False, "word": "En"},
|
|
|
|
{"entity": "I-PER", "index": 2, "score": 0.9957635998725891, "is_subword": True, "word": "##zo"},
|
|
|
|
{"entity": "I-ORG", "index": 7, "score": 0.9986497163772583, "is_subword": False, "word": "UN"},
|
2020-10-23 21:58:19 +08:00
|
|
|
],
|
|
|
|
]
|
2020-11-04 06:21:04 +08:00
|
|
|
|
2020-10-23 21:58:19 +08:00
|
|
|
expected_grouped_ner_results = [
|
|
|
|
[
|
2020-11-04 06:21:04 +08:00
|
|
|
{"entity_group": "PER", "score": 0.999369223912557, "word": "Consuelo Araújo Noguera"},
|
|
|
|
{"entity_group": "PER", "score": 0.9997771680355072, "word": "Andrés Pastrana"},
|
|
|
|
{"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc"},
|
|
|
|
],
|
|
|
|
[
|
|
|
|
{"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo"},
|
|
|
|
{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN"},
|
|
|
|
],
|
|
|
|
]
|
|
|
|
|
|
|
|
expected_grouped_ner_results_w_subword = [
|
|
|
|
[
|
|
|
|
{"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons"},
|
|
|
|
{"entity_group": "PER", "score": 0.9663328925768534, "word": "##uelo Araújo Noguera"},
|
|
|
|
{"entity_group": "PER", "score": 0.9997273534536362, "word": "Andrés Pastrana"},
|
|
|
|
{"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc"},
|
2020-10-23 21:58:19 +08:00
|
|
|
],
|
|
|
|
[
|
2020-11-04 06:21:04 +08:00
|
|
|
{"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo"},
|
|
|
|
{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN"},
|
2020-10-23 21:58:19 +08:00
|
|
|
],
|
|
|
|
]
|
|
|
|
|
|
|
|
self.assertIsNotNone(nlp)
|
|
|
|
|
|
|
|
mono_result = nlp(VALID_INPUTS[0])
|
|
|
|
self.assertIsInstance(mono_result, list)
|
|
|
|
self.assertIsInstance(mono_result[0], (dict, list))
|
|
|
|
|
|
|
|
if isinstance(mono_result[0], list):
|
|
|
|
mono_result = mono_result[0]
|
|
|
|
|
|
|
|
for key in output_keys:
|
|
|
|
self.assertIn(key, mono_result[0])
|
|
|
|
|
|
|
|
multi_result = [nlp(input) for input in VALID_INPUTS]
|
|
|
|
self.assertIsInstance(multi_result, list)
|
|
|
|
self.assertIsInstance(multi_result[0], (dict, list))
|
|
|
|
|
|
|
|
if isinstance(multi_result[0], list):
|
|
|
|
multi_result = multi_result[0]
|
|
|
|
|
|
|
|
for result in multi_result:
|
|
|
|
for key in output_keys:
|
|
|
|
self.assertIn(key, result)
|
|
|
|
|
2020-11-04 06:21:04 +08:00
|
|
|
if nlp.grouped_entities:
|
|
|
|
if nlp.ignore_subwords:
|
|
|
|
for ungrouped_input, grouped_result in zip(ungrouped_ner_inputs, expected_grouped_ner_results):
|
|
|
|
self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
|
|
|
|
else:
|
|
|
|
for ungrouped_input, grouped_result in zip(
|
|
|
|
ungrouped_ner_inputs, expected_grouped_ner_results_w_subword
|
|
|
|
):
|
|
|
|
self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
|
2020-10-23 21:58:19 +08:00
|
|
|
|
|
|
|
@require_tf
|
|
|
|
def test_tf_only(self):
|
|
|
|
model_name = "Narsil/small" # This model only has a TensorFlow version
|
|
|
|
# We test that if we don't specificy framework='tf', it gets detected automatically
|
2020-11-10 20:29:34 +08:00
|
|
|
nlp = pipeline(task="ner", model=model_name)
|
2020-10-23 21:58:19 +08:00
|
|
|
self._test_pipeline(nlp)
|
2020-11-04 06:21:04 +08:00
|
|
|
|
|
|
|
@require_tf
|
|
|
|
def test_tf_defaults(self):
|
|
|
|
for model_name in self.small_models:
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
|
|
|
nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="tf")
|
|
|
|
self._test_pipeline(nlp)
|
|
|
|
|
|
|
|
@require_tf
|
2020-11-10 20:29:34 +08:00
|
|
|
def test_tf_small_ignore_subwords_available_for_fast_tokenizers(self):
|
2020-11-04 06:21:04 +08:00
|
|
|
for model_name in self.small_models:
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
|
|
|
nlp = pipeline(
|
|
|
|
task="ner",
|
|
|
|
model=model_name,
|
|
|
|
tokenizer=tokenizer,
|
|
|
|
framework="tf",
|
|
|
|
grouped_entities=True,
|
|
|
|
ignore_subwords=True,
|
|
|
|
)
|
|
|
|
self._test_pipeline(nlp)
|
|
|
|
|
2020-11-10 20:29:34 +08:00
|
|
|
for model_name in self.small_models:
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
|
|
|
nlp = pipeline(
|
|
|
|
task="ner",
|
|
|
|
model=model_name,
|
|
|
|
tokenizer=tokenizer,
|
|
|
|
framework="tf",
|
|
|
|
grouped_entities=True,
|
|
|
|
ignore_subwords=False,
|
|
|
|
)
|
|
|
|
self._test_pipeline(nlp)
|
2020-11-04 06:21:04 +08:00
|
|
|
|
|
|
|
@require_torch
|
2020-11-10 20:29:34 +08:00
|
|
|
def test_pt_ignore_subwords_slow_tokenizer_raises(self):
|
2020-11-04 06:21:04 +08:00
|
|
|
for model_name in self.small_models:
|
2020-11-10 20:29:34 +08:00
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
|
|
|
|
|
|
|
with self.assertRaises(ValueError):
|
|
|
|
pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True)
|
|
|
|
|
|
|
|
@require_torch
|
|
|
|
def test_pt_defaults_slow_tokenizer(self):
|
|
|
|
for model_name in self.small_models:
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
2020-11-04 06:21:04 +08:00
|
|
|
nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer)
|
|
|
|
self._test_pipeline(nlp)
|
|
|
|
|
|
|
|
@require_torch
|
2020-11-10 20:29:34 +08:00
|
|
|
def test_pt_defaults(self):
|
|
|
|
for model_name in self.small_models:
|
|
|
|
nlp = pipeline(task="ner", model=model_name)
|
|
|
|
self._test_pipeline(nlp)
|
|
|
|
|
|
|
|
@require_torch
|
|
|
|
def test_pt_small_ignore_subwords_available_for_fast_tokenizers(self):
|
2020-11-04 06:21:04 +08:00
|
|
|
for model_name in self.small_models:
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
|
|
|
nlp = pipeline(
|
|
|
|
task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
|
|
|
|
)
|
|
|
|
self._test_pipeline(nlp)
|
|
|
|
|
|
|
|
for model_name in self.small_models:
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
|
|
|
|
nlp = pipeline(
|
|
|
|
task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=False
|
|
|
|
)
|
|
|
|
self._test_pipeline(nlp)
|
2020-11-10 20:29:34 +08:00
|
|
|
|
|
|
|
|
|
|
|
class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
|
|
self.args_parser = TokenClassificationArgumentHandler()
|
|
|
|
|
|
|
|
def test_simple(self):
|
|
|
|
string = "This is a simple input"
|
|
|
|
|
|
|
|
inputs, offset_mapping = self.args_parser(string)
|
|
|
|
self.assertEqual(inputs, [string])
|
|
|
|
self.assertEqual(offset_mapping, None)
|
|
|
|
|
|
|
|
inputs, offset_mapping = self.args_parser(string, string)
|
|
|
|
self.assertEqual(inputs, [string, string])
|
|
|
|
self.assertEqual(offset_mapping, None)
|
|
|
|
|
|
|
|
inputs, offset_mapping = self.args_parser(string, offset_mapping=[(0, 1), (1, 2)])
|
|
|
|
self.assertEqual(inputs, [string])
|
|
|
|
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]])
|
|
|
|
|
|
|
|
inputs, offset_mapping = self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
|
|
|
|
self.assertEqual(inputs, [string, string])
|
|
|
|
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
|
|
|
|
|
|
|
|
def test_errors(self):
|
|
|
|
string = "This is a simple input"
|
|
|
|
|
|
|
|
# 2 sentences, 1 offset_mapping
|
|
|
|
with self.assertRaises(ValueError):
|
|
|
|
self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])
|
|
|
|
|
|
|
|
# 2 sentences, 1 offset_mapping
|
|
|
|
with self.assertRaises(ValueError):
|
|
|
|
self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])
|
|
|
|
|
|
|
|
# 1 sentences, 2 offset_mapping
|
|
|
|
with self.assertRaises(ValueError):
|
|
|
|
self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
|
|
|
|
|
|
|
|
# 0 sentences, 1 offset_mapping
|
|
|
|
with self.assertRaises(ValueError):
|
|
|
|
self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])
|