mirror of
https://github.com/huggingface/transformers.git
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d8fc26e919
* NerPipeline (TokenClassification) now outputs offsets of words
- It happens that the offsets are missing, it forces the user to pattern
match the "word" from his input, which is not always feasible.
For instance if a sentence contains the same word twice, then there
is no way to know which is which.
- This PR proposes to fix that by outputting 2 new keys for this
pipelines outputs, "start" and "end", which correspond to the string
offsets of the word. That means that we should always have the
invariant:
```python
input[entity["start"]: entity["end"]] == entity["entity_group"]
# or entity["entity"] if not grouped
```
* Fixing doc style
405 lines
14 KiB
Python
405 lines
14 KiB
Python
import unittest
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from transformers import AutoTokenizer, pipeline
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from transformers.pipelines import Pipeline, TokenClassificationArgumentHandler
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from transformers.testing_utils import require_tf, require_torch, slow
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from .test_pipelines_common import CustomInputPipelineCommonMixin
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VALID_INPUTS = ["A simple string", ["list of strings"]]
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class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "ner"
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small_models = [
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"sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
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] # Default model - Models tested without the @slow decorator
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large_models = [] # Models tested with the @slow decorator
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def _test_pipeline(self, nlp: Pipeline):
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output_keys = {"entity", "word", "score", "start", "end"}
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if nlp.grouped_entities:
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output_keys = {"entity_group", "word", "score", "start", "end"}
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ungrouped_ner_inputs = [
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[
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{
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"entity": "B-PER",
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"index": 1,
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"score": 0.9994944930076599,
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"is_subword": False,
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"word": "Cons",
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"start": 0,
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"end": 4,
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},
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{
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"entity": "B-PER",
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"index": 2,
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"score": 0.8025449514389038,
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"is_subword": True,
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"word": "##uelo",
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"start": 4,
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"end": 8,
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},
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{
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"entity": "I-PER",
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"index": 3,
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"score": 0.9993102550506592,
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"is_subword": False,
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"word": "Ara",
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"start": 9,
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"end": 11,
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},
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{
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"entity": "I-PER",
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"index": 4,
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"score": 0.9993743896484375,
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"is_subword": True,
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"word": "##új",
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"start": 11,
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"end": 13,
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},
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{
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"entity": "I-PER",
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"index": 5,
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"score": 0.9992871880531311,
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"is_subword": True,
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"word": "##o",
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"start": 13,
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"end": 14,
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},
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{
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"entity": "I-PER",
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"index": 6,
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"score": 0.9993029236793518,
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"is_subword": False,
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"word": "No",
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"start": 15,
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"end": 17,
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},
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{
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"entity": "I-PER",
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"index": 7,
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"score": 0.9981776475906372,
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"is_subword": True,
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"word": "##guera",
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"start": 17,
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"end": 22,
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},
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{
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"entity": "B-PER",
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"index": 15,
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"score": 0.9998136162757874,
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"is_subword": False,
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"word": "Andrés",
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"start": 23,
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"end": 28,
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},
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{
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"entity": "I-PER",
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"index": 16,
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"score": 0.999740719795227,
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"is_subword": False,
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"word": "Pas",
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"start": 29,
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"end": 32,
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},
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{
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"entity": "I-PER",
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"index": 17,
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"score": 0.9997414350509644,
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"is_subword": True,
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"word": "##tran",
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"start": 32,
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"end": 36,
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},
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{
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"entity": "I-PER",
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"index": 18,
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"score": 0.9996136426925659,
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"is_subword": True,
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"word": "##a",
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"start": 36,
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"end": 37,
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},
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{
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"entity": "B-ORG",
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"index": 28,
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"score": 0.9989739060401917,
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"is_subword": False,
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"word": "Far",
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"start": 39,
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"end": 42,
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},
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{
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"entity": "I-ORG",
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"index": 29,
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"score": 0.7188422083854675,
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"is_subword": True,
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"word": "##c",
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"start": 42,
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"end": 43,
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},
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],
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[
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{
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"entity": "I-PER",
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"index": 1,
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"score": 0.9968166351318359,
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"is_subword": False,
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"word": "En",
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"start": 0,
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"end": 2,
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},
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{
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"entity": "I-PER",
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"index": 2,
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"score": 0.9957635998725891,
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"is_subword": True,
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"word": "##zo",
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"start": 2,
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"end": 4,
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},
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{
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"entity": "I-ORG",
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"index": 7,
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"score": 0.9986497163772583,
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"is_subword": False,
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"word": "UN",
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"start": 11,
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"end": 13,
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},
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],
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]
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expected_grouped_ner_results = [
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[
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{
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"entity_group": "PER",
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"score": 0.999369223912557,
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"word": "Consuelo Araújo Noguera",
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"start": 0,
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"end": 22,
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},
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{
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"entity_group": "PER",
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"score": 0.9997771680355072,
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"word": "Andrés Pastrana",
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"start": 23,
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"end": 37,
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},
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{"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc", "start": 39, "end": 43},
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],
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[
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{"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
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],
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]
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expected_grouped_ner_results_w_subword = [
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[
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{"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons", "start": 0, "end": 4},
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{
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"entity_group": "PER",
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"score": 0.9663328925768534,
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"word": "##uelo Araújo Noguera",
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"start": 4,
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"end": 22,
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},
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{
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"entity_group": "PER",
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"score": 0.9997273534536362,
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"word": "Andrés Pastrana",
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"start": 23,
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"end": 37,
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},
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{"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc", "start": 39, "end": 43},
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],
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[
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{"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo", "start": 0, "end": 4},
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{"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN", "start": 11, "end": 13},
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],
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]
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self.assertIsNotNone(nlp)
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mono_result = nlp(VALID_INPUTS[0])
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], (dict, list))
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if isinstance(mono_result[0], list):
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mono_result = mono_result[0]
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for key in output_keys:
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self.assertIn(key, mono_result[0])
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multi_result = [nlp(input) for input in VALID_INPUTS]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in output_keys:
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self.assertIn(key, result)
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if nlp.grouped_entities:
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if nlp.ignore_subwords:
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for ungrouped_input, grouped_result in zip(ungrouped_ner_inputs, expected_grouped_ner_results):
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self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
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else:
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for ungrouped_input, grouped_result in zip(
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ungrouped_ner_inputs, expected_grouped_ner_results_w_subword
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):
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self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
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@require_tf
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def test_tf_only(self):
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model_name = "Narsil/small" # This model only has a TensorFlow version
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# We test that if we don't specificy framework='tf', it gets detected automatically
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nlp = pipeline(task="ner", model=model_name)
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self._test_pipeline(nlp)
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@require_tf
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def test_tf_defaults(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="tf")
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self._test_pipeline(nlp)
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@require_tf
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def test_tf_small_ignore_subwords_available_for_fast_tokenizers(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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nlp = pipeline(
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task="ner",
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model=model_name,
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tokenizer=tokenizer,
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framework="tf",
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grouped_entities=True,
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ignore_subwords=True,
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)
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self._test_pipeline(nlp)
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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nlp = pipeline(
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task="ner",
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model=model_name,
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tokenizer=tokenizer,
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framework="tf",
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grouped_entities=True,
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ignore_subwords=False,
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)
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self._test_pipeline(nlp)
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@require_torch
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def test_pt_ignore_subwords_slow_tokenizer_raises(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
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with self.assertRaises(ValueError):
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pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True, use_fast=False)
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@require_torch
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def test_pt_defaults_slow_tokenizer(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer)
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self._test_pipeline(nlp)
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@require_torch
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def test_pt_defaults(self):
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for model_name in self.small_models:
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nlp = pipeline(task="ner", model=model_name)
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self._test_pipeline(nlp)
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@slow
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@require_torch
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def test_simple(self):
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nlp = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True)
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output = nlp("Hello Sarah Jessica Parker who Jessica lives in New York")
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def simplify(output):
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for i in range(len(output)):
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output[i]["score"] = round(output[i]["score"], 3)
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return output
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output = simplify(output)
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self.assertEqual(
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output,
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[
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{
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"entity_group": "PER",
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"score": 0.996,
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"word": "Sarah Jessica Parker",
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"start": 6,
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"end": 26,
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},
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{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
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{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
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],
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)
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@require_torch
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def test_pt_small_ignore_subwords_available_for_fast_tokenizers(self):
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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nlp = pipeline(
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task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
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)
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self._test_pipeline(nlp)
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for model_name in self.small_models:
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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nlp = pipeline(
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task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=False
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)
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self._test_pipeline(nlp)
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class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
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def setUp(self):
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self.args_parser = TokenClassificationArgumentHandler()
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def test_simple(self):
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string = "This is a simple input"
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inputs, offset_mapping = self.args_parser(string)
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self.assertEqual(inputs, [string])
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self.assertEqual(offset_mapping, None)
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inputs, offset_mapping = self.args_parser(string, string)
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self.assertEqual(inputs, [string, string])
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self.assertEqual(offset_mapping, None)
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inputs, offset_mapping = self.args_parser(string, offset_mapping=[(0, 1), (1, 2)])
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self.assertEqual(inputs, [string])
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self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]])
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inputs, offset_mapping = self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
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self.assertEqual(inputs, [string, string])
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self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
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def test_errors(self):
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string = "This is a simple input"
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# 2 sentences, 1 offset_mapping
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with self.assertRaises(ValueError):
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self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])
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# 2 sentences, 1 offset_mapping
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with self.assertRaises(ValueError):
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self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])
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# 1 sentences, 2 offset_mapping
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with self.assertRaises(ValueError):
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self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
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# 0 sentences, 1 offset_mapping
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with self.assertRaises(ValueError):
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self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])
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