Add bart-base (#5014)

docs/source/pretrained_models.rst

@@ -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                                                                                    |
 |                   |                                                            | (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                                                                        |
 |                   |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

src/transformers/tokenization_bart.py

@@ -28,6 +28,7 @@ logger = logging.getLogger(__name__)
 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"
 _all_bart_models = [
+    "facebook/bart-base",
     "facebook/bart-large",
     "facebook/bart-large-mnli",
     "facebook/bart-large-cnn",

tests/test_modeling_bart.py

@@ -40,6 +40,7 @@ if is_torch_available():
         BartTokenizer,
         MBartTokenizer,
         BatchEncoding,
+        pipeline,
     )
     from transformers.modeling_bart import (
         BART_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -565,6 +566,22 @@ class BartModelIntegrationTests(unittest.TestCase):
         )
         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
     def test_mnli_inference(self):