Pinyin-Code Masked LM
This repository contains a custom Transformers masked language model. Load it with
trust_remote_code=True.
Dependencies
Install the runtime dependencies before loading the model:
pip install torch transformers safetensors sentencepiece pypinyin jieba
sentencepiece is required for AutoTokenizer. pypinyin is required
for raw Mandarin-to-pinyin tokenization. jieba is required when
use_jieba is true; this export was created with use_jieba=true.
Loading
from transformers import AutoConfig, AutoModel, AutoModelForMaskedLM, AutoTokenizer
model_path = "PATH_OR_REPO_ID"
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
base_model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True)
Evaluation
Configure external evaluators with:
- model path: this local folder or Hugging Face repo ID
- backend:
masked_language_modeling - trust remote code: enabled
For BLiMP-style sentence-pair scoring, use pseudo-log-likelihood rather than left-to-right probability; this requires one forward pass per scored token.
The tokenizer accepts raw text through standard calls such as
tokenizer(text), tokenizer(text, add_special_tokens=False), and
tokenizer(texts, padding=True, truncation=True, return_tensors="pt").
It also accepts return_offsets_mapping=True for compatibility with
completion-ranking evaluators that need suffix masks. The model supports
output_hidden_states=True for representation extraction tasks.
This export sets patch_pathlib_utf8_open=true in config.json.
When loaded with trust_remote_code=True, the config installs a narrow
Windows compatibility shim so later text-mode Path.open("r") calls
without an explicit encoding default to UTF-8. Set
PINYIN_CODE_DISABLE_UTF8_OPEN_PATCH=1 before loading the model to
disable that shim.
Export metadata:
- transliteration:
pinyin-code - training_model_type:
bert - use_jieba:
true