.gitattributes

@@ -6,4 +6,3 @@
 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,62 +0,0 @@
----
-license: apache-2.0
-language: en
----
-
-# BART (large-sized model) 
-
-BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart). 
-
-Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-## Model description
-
-BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
-
-BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
-
-## Intended uses & limitations
-
-You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.
-
-### How to use
-
-Here is how to use this model in PyTorch:
-
-```python
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
-model = BartModel.from_pretrained('facebook/bart-large')
-
-inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-outputs = model(**inputs)
-
-last_hidden_states = outputs.last_hidden_state
-```
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{DBLP:journals/corr/abs-1910-13461,
-  author    = {Mike Lewis and
-               Yinhan Liu and
-               Naman Goyal and
-               Marjan Ghazvininejad and
-               Abdelrahman Mohamed and
-               Omer Levy and
-               Veselin Stoyanov and
-               Luke Zettlemoyer},
-  title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
-               Generation, Translation, and Comprehension},
-  journal   = {CoRR},
-  volume    = {abs/1910.13461},
-  year      = {2019},
-  url       = {http://arxiv.org/abs/1910.13461},
-  eprinttype = {arXiv},
-  eprint    = {1910.13461},
-  timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```

config.json

@@ -1,15 +1,16 @@
 {
-  "activation_dropout": 0.1,
+  "_num_labels": 3,
+  "activation_dropout": 0.0,
   "activation_function": "gelu",
-  "add_bias_logits": false,
   "add_final_layer_norm": false,
   "architectures": [
-    "BartModel"
+    "BartModel",
+    "BartForMaskedLM",
+    "BartForSequenceClassification"
   ],
-  "attention_dropout": 0.1,
+  "attention_dropout": 0.0,
   "bos_token_id": 0,
-  "classif_dropout": 0.1,
+  "classif_dropout": 0.0,
-  "classifier_dropout": 0.0,
   "d_model": 1024,
   "decoder_attention_heads": 16,
   "decoder_ffn_dim": 4096,
@@ -17,15 +18,11 @@
   "decoder_layers": 12,
   "decoder_start_token_id": 2,
   "dropout": 0.1,
-  "early_stopping": true,
   "encoder_attention_heads": 16,
   "encoder_ffn_dim": 4096,
   "encoder_layerdrop": 0.0,
   "encoder_layers": 12,
   "eos_token_id": 2,
-  "forced_eos_token_id": 2,
-  "forced_bos_token_id": 0,
-  "gradient_checkpointing": false,
   "id2label": {
     "0": "LABEL_0",
     "1": "LABEL_1",
@@ -40,33 +37,21 @@
   },
   "max_position_embeddings": 1024,
   "model_type": "bart",
-  "no_repeat_ngram_size": 3,
   "normalize_before": false,
-  "num_beams": 4,
   "num_hidden_layers": 12,
+  "output_past": false,
   "pad_token_id": 1,
+  "prefix": " ",
   "scale_embedding": false,
   "task_specific_params": {
     "summarization": {
-      "length_penalty": 1.0,
+      "early_stopping": true,
-      "max_length": 128,
-      "min_length": 12,
-      "num_beams": 4
-    },
-    "summarization_cnn": {
       "length_penalty": 2.0,
       "max_length": 142,
       "min_length": 56,
+      "no_repeat_ngram_size": 3,
       "num_beams": 4
-    },
-    "summarization_xsum": {
-      "length_penalty": 1.0,
-      "max_length": 62,
-      "min_length": 11,
-      "num_beams": 6
     }
   },
-  "transformers_version": "4.7.0.dev0",
-  "use_cache": true,
   "vocab_size": 50265
 }

tokenizer_config.json

@@ -1 +0,0 @@
-{"model_max_length": 1024}