INIT: add bart model

README.md

@@ -1,3 +1,44 @@
 ---
+language: 
+  - zh
 license: apache-2.0
+
+inference: true
+
+widget:
+- text: "北京是<mask>的首都"
+
+
 ---
+# Randeng-BART-139M model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
+The 139M million parameter Randeng-BART large model, using 180G Chinese data, 8 A100(40G) training for 3 days，which is a standard transformer structure.
+
+
+## Usage
+```python
+from transformers import BartForConditionalGeneration, AutoTokenizer
+import torch
+
+tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Randeng-BART-139M', use_fast=false)
+model=BartForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-BART-139M')
+
+text = '北京是<mask>的首都'
+
+logits = model(input_ids).logits
+masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
+probs = logits[0, masked_index].softmax(dim=0)
+values, predictions = probs.topk(1)
+
+print(tokenizer.decode(predictions))
+```
+
+## Citation
+If you find the resource is useful, please cite the following website in your paper.
+```
+@misc{Fengshenbang-LM,
+  title={Fengshenbang-LM},
+  author={IDEA-CCNL},
+  year={2022},
+  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
+}
+```

added_tokens.json

@@ -0,0 +1 @@
+{"<pad>": 40000, "<mask>": 40001}

config.json

@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "bart-base",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "BartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 2,
+  "dropout": 0.1,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "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_position_embeddings": 1024,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "normalize_embedding": true,
+  "num_beams": 4,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "scale_embedding": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.16.0.dev0",
+  "use_cache": true,
+  "vocab_size": 50265
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:feeaeccf52742bf4cd252aa67a3153637983e2334f41971d7c60bc4aad981f84
+size 279034105

save_tokenizer.py

@@ -0,0 +1,9 @@
+from transformers import T5Tokenizer
+
+tokenizer = T5Tokenizer.from_pretrained(
+    '/cognitive_comp/common_data/tokenizers/sentence_piece_bpe/bpe_v40000_s42_cov0.9995_max6_corpus1M.model',
+    additional_special_tokens=['<s>', '<mask>'],
+    extra_ids=0)
+tokenizer.bos_token = '<s>'
+tokenizer.mask_token = '<mask>'
+tokenizer.save_pretrained('/cognitive_comp/gaoxinyu/pretrained_model/bart-base')

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "additional_special_tokens": ["<s>", "<mask>"]}

spiece.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a1836aa16c5e41fb9bec14c477218b83812919d19dfdde1c49a419cd9935615
+size 858518

tokenizer_config.json

@@ -0,0 +1,13 @@
+{
+    "eos_token": "</s>",
+    "unk_token": "<unk>",
+    "pad_token": "<pad>",
+    "extra_ids": 0,
+    "additional_special_tokens": [
+        "<s>",
+        "<mask>"
+    ],
+    "sp_model_kwargs": {},
+    "name_or_path": "/cognitive_comp/common_data/tokenizers/sentence_piece_bpe/bpe_v40000_s42_cov0.9995_max6_corpus1M.model",
+    "tokenizer_class": "T5Tokenizer"
+}