re-upload bart

scripts/conversion_toolkits/README.md

@@ -12,7 +12,7 @@ The testing step mentioned above are controlled by the flag `--test`, in which t
 tolerance of 1e-3 between gluon model with converted weights and original tensorflow model.
 In addition, we can use GPU in all converting scripts by adding `--gpu 0`.
 
-For RoBERTa XLM-R and BART model, please instal the [fairseq](https://github.com/pytorch/fairseq#requirements-and-installation) package locally before conversion.
+For RoBERTa XLM-R and BART model, please instal the [fairseq](https://github.com/pytorch/fairseq#requirements-and-installation) package locally as `pip install git+https://github.com/pytorch/fairseq.git@master`.
 
 ## BERT
 Convert model from [BERT LIST](https://tfhub.dev/google/collections/bert/1).

scripts/conversion_toolkits/convert_bart.sh

@@ -0,0 +1,7 @@
+for model in base large
+do
+    mkdir bart_${model}
+    wget  "https://dl.fbaipublicfiles.com/fairseq/models/bart.${model}.tar.gz"
+    tar zxf bart.${model}.tar.gz --directory bart_${model}
+    python convert_fairseq_bart.py --fairseq_model_path bart_${model}/bart.${model} --test
+done

src/gluonnlp/models/bart.py

@@ -127,14 +127,14 @@ def bart_large():
         'cfg': bart_base(),
         'merges': 'fairseq_bart_base/gpt2-396d4d8e.merges',
         'vocab': 'fairseq_bart_base/gpt2-f4dedacb.vocab',
-        'params': 'fairseq_bart_base/model-6dea1e11.params',
+        'params': 'fairseq_bart_base/model-8f4929b5.params',
         'lowercase': False,
     },
     'fairseq_bart_large': {
         'cfg': bart_large(),
         'merges': 'fairseq_bart_large/gpt2-396d4d8e.merges',
         'vocab': 'fairseq_bart_large/gpt2-f1335494.vocab',
-        'params': 'fairseq_bart_large/model-38f35552.params',
+        'params': 'fairseq_bart_large/model-862277b1.params',
         'lowercase': False,
     }
 }

src/gluonnlp/models/model_zoo_checksums/bart.txt

@@ -1,8 +1,8 @@
-fairseq_bart_base/model-6dea1e11.params   6dea1e11049f408cdd1088cd2c7e418434e1b03c    560560736
+fairseq_bart_base/model-8f4929b5.params   8f4929b54f2f77619885cea9f3bd7dba51a27f38    560560748
 fairseq_bart_base/gpt2-396d4d8e.merges    396d4d8ec90cb02f4d56e049e0e4add868bcd943    456318
-fairseq_bart_base/model-3a06f8c8.yml      3a06f8c815fdda7c6efdb7859f7c7174895272b2    751
+fairseq_bart_base/model-251bf089.yml      251bf08944d18cc29b59a4a854bdbccf601dabb5    754
 fairseq_bart_base/gpt2-f4dedacb.vocab     f4dedacb076b1df441c9c7398ed9acd3c19865f3    575079
-fairseq_bart_large/model-38f35552.params  38f35552a8889593f8f200074244a3c8e6d52a0c    1625180950
+fairseq_bart_large/model-862277b1.params  862277b1489ed95140cb63279fbd0098ef2dea90    1625180962
 fairseq_bart_large/gpt2-396d4d8e.merges   396d4d8ec90cb02f4d56e049e0e4add868bcd943    456318
-fairseq_bart_large/model-b52f8ac1.yml     b52f8ac182573d63a18a5f14900f457531177f31    743
+fairseq_bart_large/model-a2932dea.yml     a2932deaf9737d95891755841fae3e388f3d698a    746
 fairseq_bart_large/gpt2-f1335494.vocab    f1335494f47917829e3b1d08e579ff2c3fe4fd60    558231