Fix CI after killing archive maps (#4724)

* 🐛 Fix model ids for BART and Flaubert

docs/source/model_doc/bart.rst

@@ -22,7 +22,7 @@ Implementation Notes
 - The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``)  if they are not passed. This is different than some other modeling APIs.
 - Model predictions are intended to be identical to the original implementation. This only works, however, if the string you pass to ``fairseq.encode`` starts with a space.
 - ``BartForConditionalGeneration.generate`` should be used for conditional generation tasks like summarization, see the example in that docstrings
-- Models that load the ``"bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
+- Models that load the ``"facebook/bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
 
 
 

examples/summarization/bart/evaluate_cnn.py

@@ -21,7 +21,7 @@ def generate_summaries(
 ):
     fout = Path(out_file).open("w")
     model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
-    tokenizer = BartTokenizer.from_pretrained("bart-large")
+    tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
 
     max_length = 140
     min_length = 55
@@ -54,7 +54,7 @@ def run_generate():
         "output_path", type=str, help="where to save summaries",
     )
     parser.add_argument(
-        "model_name", type=str, default="bart-large-cnn", help="like bart-large-cnn",
+        "model_name", type=str, default="facebook/bart-large-cnn", help="like bart-large-cnn",
     )
     parser.add_argument(
         "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",

examples/summarization/bart/test_bart_examples.py

@@ -129,7 +129,7 @@ class TestBartExamples(unittest.TestCase):
         summaries = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
         _dump_articles((tmp_dir / "train.source"), articles)
         _dump_articles((tmp_dir / "train.target"), summaries)
-        tokenizer = BartTokenizer.from_pretrained("bart-large")
+        tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
         max_len_source = max(len(tokenizer.encode(a)) for a in articles)
         max_len_target = max(len(tokenizer.encode(a)) for a in summaries)
         trunc_target = 4

src/transformers/configuration_ctrl.py

@@ -22,7 +22,7 @@ from .configuration_utils import PretrainedConfig
 
 logger = logging.getLogger(__name__)
 
-CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://storage.googleapis.com/sf-ctrl/pytorch/ctrl-config.json"}
+CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://s3.amazonaws.com/models.huggingface.co/bert/ctrl-config.json"}
 
 
 class CTRLConfig(PretrainedConfig):

src/transformers/convert_bart_original_pytorch_checkpoint_to_pytorch.py

@@ -124,7 +124,7 @@ def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path, hf_checkp
         remove_ignore_keys_(state_dict)
         state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
         fairseq_output = bart.extract_features(tokens)
-        if hf_checkpoint_name == "bart-large":
+        if hf_checkpoint_name == "facebook/bart-large":
             model = BartModel(config).eval()
             model.load_state_dict(state_dict)
             new_model_outputs = model(tokens).model[0]

src/transformers/pipelines.py

@@ -1633,7 +1633,7 @@ SUPPORTED_TASKS = {
         "impl": SummarizationPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {"model": {"pt": "bart-large-cnn", "tf": "t5-small"}, "config": None, "tokenizer": None},
+        "default": {"model": {"pt": "facebook/bart-large-cnn", "tf": "t5-small"}, "config": None, "tokenizer": None},
     },
     "translation_en_to_fr": {
         "impl": TranslationPipeline,

src/transformers/tokenization_bart.py

@@ -25,7 +25,12 @@ logger = logging.getLogger(__name__)
 # vocab and merges same as roberta
 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 = ["bart-large", "bart-large-mnli", "bart-large-cnn", "bart-large-xsum"]
+_all_bart_models = [
+    "facebook/bart-large",
+    "facebook/bart-large-mnli",
+    "facebook/bart-large-cnn",
+    "facebook/bart-large-xsum",
+]
 
 
 class BartTokenizer(RobertaTokenizer):
@@ -37,7 +42,7 @@ class BartTokenizer(RobertaTokenizer):
     }
 
 
-_all_mbart_models = ["mbart-large-en-ro"]
+_all_mbart_models = ["facebook/mbart-large-en-ro"]
 SPM_URL = "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/sentence.bpe.model"
 
 

src/transformers/tokenization_flaubert.py

@@ -32,31 +32,31 @@ VOCAB_FILES_NAMES = {
 
 PRETRAINED_VOCAB_FILES_MAP = {
     "vocab_file": {
-        "flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/vocab.json",
+        "flaubert/flaubert_small_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/vocab.json",
-        "flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/vocab.json",
+        "flaubert/flaubert_base_uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/vocab.json",
-        "flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/vocab.json",
+        "flaubert/flaubert_base_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/vocab.json",
-        "flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/vocab.json",
+        "flaubert/flaubert_large_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/vocab.json",
     },
     "merges_file": {
-        "flaubert-small-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/merges.txt",
+        "flaubert/flaubert_small_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/merges.txt",
-        "flaubert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/merges.txt",
+        "flaubert/flaubert_base_uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_uncased/merges.txt",
-        "flaubert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/merges.txt",
+        "flaubert/flaubert_base_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_base_cased/merges.txt",
-        "flaubert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/merges.txt",
+        "flaubert/flaubert_large_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_large_cased/merges.txt",
     },
 }
 
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "flaubert-small-cased": 512,
+    "flaubert/flaubert_small_cased": 512,
-    "flaubert-base-uncased": 512,
+    "flaubert/flaubert_base_uncased": 512,
-    "flaubert-base-cased": 512,
+    "flaubert/flaubert_base_cased": 512,
-    "flaubert-large-cased": 512,
+    "flaubert/flaubert_large_cased": 512,
 }
 
 PRETRAINED_INIT_CONFIGURATION = {
-    "flaubert-small-cased": {"do_lowercase": False},
+    "flaubert/flaubert_small_cased": {"do_lowercase": False},
-    "flaubert-base-uncased": {"do_lowercase": True},
+    "flaubert/flaubert_base_uncased": {"do_lowercase": True},
-    "flaubert-base-cased": {"do_lowercase": False},
+    "flaubert/flaubert_base_cased": {"do_lowercase": False},
-    "flaubert-large-cased": {"do_lowercase": False},
+    "flaubert/flaubert_large_cased": {"do_lowercase": False},
 }