init

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README.md

@@ -1,3 +1,46 @@
 ---
+tags:
+- text2text-generation
+- Chinese
+- seq2seq
+- grammar
+language: zh
 license: apache-2.0
 ---
+# Pseudo-Native-BART-CGEC
+
+This model is a cutting-edge CGEC model based on [Chinese BART-large](https://huggingface.co/fnlp/bart-large-chinese).
+It is trained with about 100M pseudo native speaker CGEC training data generated by heuristic rules.
+More details can be found in our [Github](https://github.com/HillZhang1999/NaSGEC) and the [paper]().
+
+## Usage
+
+pip install transformers
+
+```
+from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
+tokenizer = BertTokenizer.from_pretrained("HillZhang/pseudo_native_bart_CGEC")
+model = BartForConditionalGeneration.from_pretrained("HillZhang/pseudo_native_bart_CGEC")
+encoded_input = tokenizer(["北京是中国的都。", "他说：”我最爱的运动是打蓝球“", "我每天大约喝5次水左右。", "今天，我非常开开心。"], return_tensors="pt", padding=True, truncation=True)
+if "token_type_ids" in encoded_input:
+    del encoded_input["token_type_ids"]
+output = model.generate(**encoded_input)
+print(tokenizer.batch_decode(output, skip_special_tokens=True))
+```
+
+## Citation
+
+```
+@inproceedings{zhang-etal-2023-nasgec,
+    title = "{Na}{SGEC}: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts",
+    author = "Zhang, Yue  and
+      Zhang, Bo  and
+      Jiang, Haochen  and
+      Li, Zhenghua  and
+      Li, Chen  and
+      Huang, Fei  and
+      Zhang, Min"
+    booktitle = "Findings of ACL",
+    year = "2023"
+    }
+```

config.json

@@ -0,0 +1,78 @@
+{
+  "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,
+  "unk_token_id": 3,
+  "classif_dropout": 0.1,
+  "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,
+  "do_blenderbot_90_layernorm": false,
+  "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,
+  "extra_pos_embeddings": 2,
+  "force_bos_token_to_be_generated": true,
+  "forced_bos_token_id": 0,
+  "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_position_embeddings": 512,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "normalize_embedding": true,
+  "num_beams": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "scale_embedding": false,
+  "static_position_embeddings": false,
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "transformers_version": "4.4.1",
+  "use_cache": true,
+  "vocab_size": 21132
+}

pytorch_model.bin

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+oid sha256:a6272627867d55d96f6c3cd66361a6bf83712b2dc99607c9f94bbc1f333dbbfa
+size 1501922415

special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "bos_token": "<s>", "eos_token": "</s>"}