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Add bart-base (#5014)

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Sam Shleifer 2020-06-15 13:29:26 -04:00 committed by GitHub
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2
docs/source/pretrained_models.rst
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@ -278,6 +278,8 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
| Bart | ``facebook/bart-large`` | | 24-layer, 1024-hidden, 16-heads, 406M parameters | | Bart | ``facebook/bart-large`` | | 24-layer, 1024-hidden, 16-heads, 406M parameters |
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_) | | | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``facebook/bart-base`` | | 12-layer, 768-hidden, 16-heads, 139M parameters |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``facebook/bart-large-mnli`` | | Adds a 2 layer classification head with 1 million parameters | | | ``facebook/bart-large-mnli`` | | Adds a 2 layer classification head with 1 million parameters |
| | | | bart-large base architecture with a classification head, finetuned on MNLI | | | | | bart-large base architecture with a classification head, finetuned on MNLI |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+ | +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

1
src/transformers/tokenization_bart.py
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@ -28,6 +28,7 @@ logger = logging.getLogger(__name__)
vocab_url = "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json" vocab_url = "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json"
merges_url = "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt" merges_url = "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt"
_all_bart_models = [ _all_bart_models = [
"facebook/bart-base",
"facebook/bart-large", "facebook/bart-large",
"facebook/bart-large-mnli", "facebook/bart-large-mnli",
"facebook/bart-large-cnn", "facebook/bart-large-cnn",

17
tests/test_modeling_bart.py
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@ -40,6 +40,7 @@ if is_torch_available():
BartTokenizer, BartTokenizer,
MBartTokenizer, MBartTokenizer,
BatchEncoding, BatchEncoding,
pipeline,
) )
from transformers.modeling_bart import ( from transformers.modeling_bart import (
BART_PRETRAINED_MODEL_ARCHIVE_LIST, BART_PRETRAINED_MODEL_ARCHIVE_LIST,
@ -565,6 +566,22 @@ class BartModelIntegrationTests(unittest.TestCase):
) )
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
@slow
def test_bart_base_mask_filling(self):
pbase = pipeline(task="fill-mask", model="facebook/bart-base")
src_text = [" I went to the <mask>."]
results = [x["token_str"] for x in pbase(src_text)]
expected_results = ["Ġbathroom", "Ġrestroom", "Ġhospital", "Ġkitchen", "Ġcar"]
self.assertListEqual(results, expected_results)
@slow
def test_bart_large_mask_filling(self):
pbase = pipeline(task="fill-mask", model="facebook/bart-large")
src_text = [" I went to the <mask>."]
results = [x["token_str"] for x in pbase(src_text)]
expected_results = ["Ġbathroom", "Ġgym", "Ġwrong", "Ġmovies", "Ġhospital"]
self.assertListEqual(results, expected_results)
@slow @slow
def test_mnli_inference(self): def test_mnli_inference(self):