lsy

.gitattributes

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 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
-model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,73 +1,3 @@
 ---
 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")
-
-```

model.safetensors

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tokenizer_config.json

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