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Add missing token classification for XLM (#3277)

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* Add the missing token classification for XLM

* fix styling

* Add XLMForTokenClassification to AutoModelForTokenClassification class

* Fix docstring typo for non-existing class

* Add the missing token classification for XLM

* fix styling

* fix styling

* Add XLMForTokenClassification to AutoModelForTokenClassification class

* Fix docstring typo for non-existing class

* Add missing description for AlbertForTokenClassification

* fix styling

* Add missing docstring for AlBert

* Slow tests should be slow

Co-authored-by: Sakares Saengkaew <s.sakares@gmail.com>
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
sakares saengkaew
2020-03-26 21:22:13 +07:00
committed by GitHub
parent 311970546f
commit 1a6c546c6f
5 changed files with 169 additions and 22 deletions
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17
tests/test_modeling_auto.py
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@@ -37,6 +37,8 @@ if is_torch_available():
BertForSequenceClassification,
AutoModelForQuestionAnswering,
BertForQuestionAnswering,
AutoModelForTokenClassification,
BertForTokenClassification,
)
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_auto import (
@@ -109,7 +111,7 @@ class AutoModelTest(unittest.TestCase):
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
# @slow
@slow
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
@@ -122,6 +124,19 @@ class AutoModelTest(unittest.TestCase):
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
@slow
def test_token_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForTokenClassification.from_pretrained(model_name)
model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForTokenClassification)
def test_from_pretrained_identifier(self):
logging.basicConfig(level=logging.INFO)
model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)