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README.md

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 ---
-license: apache-2.0
+language: 
+- multilingual
+- ar 
+- cs
+- de
+- en
+- es
+- et
+- fi
+- fr
+- gu
+- hi
+- it
+- ja
+- kk
+- ko
+- lt
+- lv
+- my
+- ne
+- nl
+- ro
+- ru
+- si
+- tr
+- vi
+- zh
+- af
+- az
+- bn
+- fa
+- he
+- hr
+- id
+- ka
+- km
+- mk
+- ml
+- mn
+- mr
+- pl
+- ps
+- pt
+- sv
+- sw
+- ta
+- te
+- th
+- tl
+- uk
+- ur
+- xh
+- gl
+- sl
+license: mit
+tags:
+- mbart-50
 ---
+
+# mBART-50
+
+mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper.
+
+## Model description
+
+mBART-50 is a multilingual Sequence-to-Sequence model. It was introduced to show that multilingual translation models can be created through multilingual fine-tuning. 
+Instead of fine-tuning on one direction, a pre-trained model is fine-tuned on many directions simultaneously. mBART-50 is created using the original mBART model and extended to add extra 25 languages to support multilingual machine translation models of 50 languages. The pre-training objective is explained below.
+
+**Multilingual Denoising Pretraining**: The model incorporates N languages by concatenating data: 
+`D = {D1, ..., DN }` where each Di is a collection of monolingual documents in language `i`. The source documents are noised using two schemes, 
+first randomly shuffling the original sentences' order, and second a novel in-filling scheme, 
+where spans of text are replaced with a single mask token. The model is then tasked to reconstruct the original text. 
+35% of each instance's words are masked by random sampling a span length according to a Poisson distribution `(λ = 3.5)`.
+The decoder input is the original text with one position offset. A language id symbol `LID` is used as the initial token to predict the sentence.
+
+
+## Intended uses & limitations
+
+`mbart-large-50` is pre-trained model and primarily aimed at being fine-tuned on translation tasks. It can also be fine-tuned on other multilingual sequence-to-sequence tasks. 
+See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for fine-tuned versions.
+
+
+## Training
+
+As the model is multilingual, it expects the sequences in a different format. A special language id token is used as a prefix in both the source and target text. The text format is `[lang_code] X [eos]` with `X` being the source or target text respectively and `lang_code` is `source_lang_code` for source text and `tgt_lang_code` for target text. `bos` is never used. Once the examples are prepared in this format, it can be trained as any other sequence-to-sequence model.
+
+
+```python
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+
+model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50")
+tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
+
+src_text = " UN Chief Says There Is No Military Solution in Syria"
+tgt_text =  "Şeful ONU declară că nu există o soluţie militară în Siria"
+
+model_inputs = tokenizer(src_text, return_tensors="pt")
+with tokenizer.as_target_tokenizer():
+    labels = tokenizer(tgt_text, return_tensors="pt").input_ids
+
+model(**model_inputs, labels=labels) # forward pass
+```
+
+
+
+## Languages covered
+Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)
+
+
+## BibTeX entry and citation info
+```
+@article{tang2020multilingual,
+    title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
+    author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},
+    year={2020},
+    eprint={2008.00401},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "_name_or_path": "/home/suraj/projects/mbart-50/hf_models/mbart-50-large",
+  "_num_labels": 3,
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": true,
+  "architectures": [
+    "MBartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "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,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_length": 200,
+  "max_position_embeddings": 1024,
+  "model_type": "mbart",
+  "normalize_before": true,
+  "normalize_embedding": true,
+  "num_beams": 5,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "scale_embedding": true,
+  "static_position_embeddings": false,
+  "transformers_version": "4.4.0.dev0",
+  "use_cache": true,
+  "vocab_size": 250054,
+  "tokenizer_class": "MBart50Tokenizer"
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "early_stopping": true,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "max_length": 200,
+  "num_beams": 5,
+  "pad_token_id": 1,
+  "transformers_version": "4.27.0.dev0"
+}

pytorch_model.bin

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sentencepiece.bpe.model

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special_tokens_map.json

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tf_model.h5

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tokenizer_config.json

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