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test_pipelines_ner.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import AutoTokenizer, is_torch_available, pipeline
from transformers.pipelines import Pipeline, TokenClassificationArgumentHandler
from transformers.testing_utils import require_tf, require_torch, slow
from .test_pipelines_common import CustomInputPipelineCommonMixin
if is_torch_available():
import numpy as np
VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
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", "start", "end"}
if nlp.grouped_entities:
output_keys = {"entity_group", "word", "score", "start", "end"}
ungrouped_ner_inputs = [
[
{
"entity": "B-PER",
"index": 1,
"score": 0.9994944930076599,
"is_subword": False,
"word": "Cons",
"start": 0,
"end": 4,
},
{
"entity": "B-PER",
"index": 2,
"score": 0.8025449514389038,
"is_subword": True,
"word": "##uelo",
"start": 4,
"end": 8,
},
{
"entity": "I-PER",
"index": 3,
"score": 0.9993102550506592,
"is_subword": False,
"word": "Ara",
"start": 9,
"end": 11,
},
{
"entity": "I-PER",
"index": 4,
"score": 0.9993743896484375,
"is_subword": True,
"word": "##új",
"start": 11,
"end": 13,
},
{
"entity": "I-PER",
"index": 5,
"score": 0.9992871880531311,
"is_subword": True,
"word": "##o",
"start": 13,
"end": 14,
},
{
"entity": "I-PER",
"index": 6,
"score": 0.9993029236793518,
"is_subword": False,
"word": "No",
"start": 15,
"end": 17,
},
{
"entity": "I-PER",
"index": 7,
"score": 0.9981776475906372,
"is_subword": True,
"word": "##guera",
"start": 17,
"end": 22,
},
{
"entity": "B-PER",
"index": 15,
"score": 0.9998136162757874,
"is_subword": False,
"word": "Andrés",
"start": 23,
"end": 28,
},
{
"entity": "I-PER",
"index": 16,
"score": 0.999740719795227,
"is_subword": False,
"word": "Pas",
"start": 29,
"end": 32,
},
{
"entity": "I-PER",
"index": 17,
"score": 0.9997414350509644,
"is_subword": True,
"word": "##tran",
"start": 32,
"end": 36,
},
{
"entity": "I-PER",
"index": 18,
"score": 0.9996136426925659,
"is_subword": True,
"word": "##a",
"start": 36,
"end": 37,
},
{
"entity": "B-ORG",
"index": 28,
"score": 0.9989739060401917,
"is_subword": False,
"word": "Far",
"start": 39,
"end": 42,
},
{
"entity": "I-ORG",
"index": 29,
"score": 0.7188422083854675,
"is_subword": True,
"word": "##c",
"start": 42,
"end": 43,
},
],
[
{
"entity": "I-PER",
"index": 1,
"score": 0.9968166351318359,
"is_subword": False,
"word": "En",
"start": 0,
"end": 2,
},
{
"entity": "I-PER",
"index": 2,
"score": 0.9957635998725891,
"is_subword": True,
"word": "##zo",
"start": 2,
"end": 4,
},
{
"entity": "I-ORG",
"index": 7,
"score": 0.9986497163772583,
"is_subword": False,
"word": "UN",
"start": 11,
"end": 13,
},
],
]
expected_grouped_ner_results = [
[
{
"entity_group": "PER",
"score": 0.999369223912557,
"word": "Consuelo Araújo Noguera",
"start": 0,
"end": 22,
},
{
"entity_group": "PER",
"score": 0.9997771680355072,
"word": "Andrés Pastrana",
"start": 23,
"end": 37,
},
{"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc", "start": 39, "end": 43},
],
[
{"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
],
]
expected_grouped_ner_results_w_subword = [
[
{"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons", "start": 0, "end": 4},
{
"entity_group": "PER",
"score": 0.9663328925768534,
"word": "##uelo Araújo Noguera",
"start": 4,
"end": 22,
},
{
"entity_group": "PER",
"score": 0.9997273534536362,
"word": "Andrés Pastrana",
"start": 23,
"end": 37,
},
{"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc", "start": 39, "end": 43},
],
[
{"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
],
]
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)
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)
@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
nlp = pipeline(task="ner", model=model_name)
self._test_pipeline(nlp)
@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
def test_tf_small_ignore_subwords_available_for_fast_tokenizers(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",
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,
framework="tf",
grouped_entities=True,
ignore_subwords=False,
)
self._test_pipeline(nlp)
@require_torch
def test_pt_ignore_subwords_slow_tokenizer_raises(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
with self.assertRaises(ValueError):
pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True, use_fast=False)
@require_torch
def test_pt_defaults_slow_tokenizer(self):
for model_name in self.small_models:
tokenizer = AutoTokenizer.from_pretrained(model_name)
nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer)
self._test_pipeline(nlp)
@require_torch
def test_pt_defaults(self):
for model_name in self.small_models:
nlp = pipeline(task="ner", model=model_name)
self._test_pipeline(nlp)
@slow
@require_torch
def test_simple(self):
nlp = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True)
sentence = "Hello Sarah Jessica Parker who Jessica lives in New York"
sentence2 = "This is a simple test"
output = nlp(sentence)
def simplify(output):
if isinstance(output, (list, tuple)):
return [simplify(item) for item in output]
elif isinstance(output, dict):
return {simplify(k): simplify(v) for k, v in output.items()}
elif isinstance(output, (str, int, np.int64)):
return output
elif isinstance(output, float):
return round(output, 3)
else:
raise Exception(f"Cannot handle {type(output)}")
output_ = simplify(output)
self.assertEqual(
output_,
[
{
"entity_group": "PER",
"score": 0.996,
"word": "Sarah Jessica Parker",
"start": 6,
"end": 26,
},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
)
output = nlp([sentence, sentence2])
output_ = simplify(output)
self.assertEqual(
output_,
[
[
{"entity_group": "PER", "score": 0.996, "word": "Sarah Jessica Parker", "start": 6, "end": 26},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
[],
],
)
@require_torch
def test_pt_small_ignore_subwords_available_for_fast_tokenizers(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, 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)
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, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, input_list
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(TypeError):
self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])

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