Fill-Mask
Transformers
Safetensors
pinyin_code
masked-lm
trust-remote-code
sentencepiece
custom_code
Instructions to use timorobrecht/full_chinese_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timorobrecht/full_chinese_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="timorobrecht/full_chinese_bert", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("timorobrecht/full_chinese_bert", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "PinyinCodeForMaskedLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_pinyin_code.PinyinCodeConfig", | |
| "AutoModel": "modeling_pinyin_code.PinyinCodeEncoderModel", | |
| "AutoModelForMaskedLM": "modeling_pinyin_code.PinyinCodeForMaskedLM", | |
| "AutoTokenizer": [ | |
| "tokenization_pinyin_code.EncodedMandarinTokenizer", | |
| null | |
| ] | |
| }, | |
| "block_size": 512, | |
| "bos_token_id": 2, | |
| "cls_token_id": 2, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "eos_token_id": 3, | |
| "evaluation_backend": "masked_language_modeling", | |
| "hidden_size": 512, | |
| "is_decoder": false, | |
| "mask_token_id": 14, | |
| "max_position_embeddings": 512, | |
| "model_type": "pinyin_code", | |
| "n_embd": 512, | |
| "n_head": 8, | |
| "n_layer": 8, | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 8, | |
| "pad_token_id": 0, | |
| "patch_pathlib_utf8_open": true, | |
| "sep_token_id": 3, | |
| "training_model_type": "bert", | |
| "transformers_version": "5.10.2", | |
| "unk_token_id": 1, | |
| "use_cache": false, | |
| "vocab_size": 16000 | |
| } | |