Upload 10 files
Browse files- bert-base-chinese/.gitattributes +10 -0
- bert-base-chinese/README.md +75 -0
- bert-base-chinese/config.json +25 -0
- bert-base-chinese/flax_model.msgpack +3 -0
- bert-base-chinese/model.safetensors +3 -0
- bert-base-chinese/pytorch_model.bin +3 -0
- bert-base-chinese/tf_model.h5 +3 -0
- bert-base-chinese/tokenizer.json +0 -0
- bert-base-chinese/tokenizer_config.json +1 -0
- bert-base-chinese/vocab.txt +0 -0
bert-base-chinese/.gitattributes
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bert-base-chinese/README.md
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---
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language: zh
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---
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# Bert-base-chinese
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## Table of Contents
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- [Model Details](#model-details)
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- [Uses](#uses)
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- [Risks, Limitations and Biases](#risks-limitations-and-biases)
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- [Training](#training)
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- [Evaluation](#evaluation)
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- [How to Get Started With the Model](#how-to-get-started-with-the-model)
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## Model Details
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### Model Description
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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).
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- **Developed by:** HuggingFace team
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- **Model Type:** Fill-Mask
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- **Language(s):** Chinese
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- **License:** [More Information needed]
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- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
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### Model Sources
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- **Paper:** [BERT](https://arxiv.org/abs/1810.04805)
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## Uses
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#### Direct Use
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This model can be used for masked language modeling
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## Risks, Limitations and Biases
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**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
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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)).
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## Training
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#### Training Procedure
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* **type_vocab_size:** 2
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* **vocab_size:** 21128
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* **num_hidden_layers:** 12
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#### Training Data
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[More Information Needed]
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## Evaluation
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#### Results
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[More Information Needed]
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## How to Get Started With the Model
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```python
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
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model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
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```
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bert-base-chinese/config.json
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{
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"type_vocab_size": 2,
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"vocab_size": 21128
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}
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bert-base-chinese/flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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size 409168515
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bert-base-chinese/model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 411553788
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bert-base-chinese/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 411577189
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bert-base-chinese/tf_model.h5
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version https://git-lfs.github.com/spec/v1
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size 478309336
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bert-base-chinese/tokenizer.json
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bert-base-chinese/tokenizer_config.json
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{"do_lower_case": false, "model_max_length": 512}
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bert-base-chinese/vocab.txt
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