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.gitattributes

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-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.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

README.md

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----
-language: zh 
-widget: text: "中国的首都是[MASK]京。"
-thumbnail: 
-tags:
-license: 
-datasets:
-metrics:
----
-
-# MyModelName
-
-## Model description
-
-## Intended uses & limitations
-
-#### How to use
-
-You can use this model directly with a pipeline for masked language modeling:
-
-```python
->>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='hhou435/chinese_roberta_L-2_H-128')
->>> unmasker("中国的首都是[MASK]京。")
-[
-    {'sequence': '[CLS] 中 国 的 首 都 是 北 京 。 [SEP]', 
-     'score': 0.9427323937416077, 
-     'token': 1266, 
-     'token_str': '北'}, 
-    {'sequence': '[CLS] 中 国 的 首 都 是 南 京 。 [SEP]',
-     'score': 0.029202355071902275, 
-     'token': 1298,
-     'token_str': '南'}, 
-    {'sequence': '[CLS] 中 国 的 首 都 是 东 京 。 [SEP]',
-     'score': 0.00977553054690361,
-     'token': 691, 
-     'token_str': '东'}, 
-    {'sequence': '[CLS] 中 国 的 首 都 是 葡 京 。 [SEP]',
-     'score': 0.00489805219694972,
-     'token': 5868, 
-     'token_str': '葡'},
-    {'sequence': '[CLS] 中 国 的 首 都 是 新 京 。 [SEP]',
-     'score': 0.0027360401581972837, 
-     'token': 3173, 
-     'token_str': '新'}
-]
-
-```
-
-Here is how to use this model to get the features of a given text in PyTorch:
-
-```python
-from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('hhou435/chinese_roberta_L-2_H-128')
-model = BertModel.from_pretrained("hhou435/chinese_roberta_L-2_H-128")
-text = "用你喜欢的任何文本替换我。"
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-```
-
-and in TensorFlow:
-
-```python
-from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('hhou435/chinese_roberta_L-2_H-128')
-model = TFBertModel.from_pretrained("hhou435/chinese_roberta_L-2_H-128")
-text = "用你喜欢的任何文本替换我。"
-encoded_input = tokenizer(text, return_tensors='tf')
-output = model(encoded_input)
-```

config.json

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-{
-  "architectures": [
-    "BertForMaskedLM"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 128,
-  "initializer_range": 0.02,
-  "intermediate_size": 512,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 2,
-  "num_hidden_layers": 2,
-  "pad_token_id": 0,
-  "type_vocab_size": 2,
-  "vocab_size": 21128
-}

pytorch_model.bin

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special_tokens_map.json

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-{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tf_model.h5

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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, "model_max_length": 512}