Duplicate from google-bert/bert-base-chinese

Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+language: zh
+license: apache-2.0
+---
+
+# Bert-base-chinese
+
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+
+
+## Model Details
+
+### Model Description
+
+This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper).
+
+- **Developed by:** Google
+- **Model Type:** Fill-Mask
+- **Language(s):** Chinese
+- **License:** Apache 2.0
+- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
+
+### Model Sources
+- **GitHub repo**: https://github.com/google-research/bert/blob/master/multilingual.md
+- **Paper:** [BERT](https://arxiv.org/abs/1810.04805)
+
+
+## Uses
+
+#### Direct Use
+
+This model can be used for masked language modeling 
+
+
+
+## Risks, Limitations and Biases
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+
+
+## Training
+
+#### Training Procedure
+* **type_vocab_size:** 2
+* **vocab_size:** 21128
+* **num_hidden_layers:** 12
+
+#### Training Data
+[More Information Needed]
+
+## Evaluation
+
+#### Results
+
+[More Information Needed]
+
+
+## How to Get Started With the Model
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
+
+model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
+
+```

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

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tokenizer_config.json

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+{"do_lower_case": false, "model_max_length": 512}