semvec/pypi-packages huggingface dataset:
prolm 0.1.1
ProLM model utilities
License: MIT
Requires Python: >=3.9

# ProLM

## 安装说明

pip install prolm

## 分词器使用说明

### 1. Tokenize
输入格式为 <tag>content</tag>...<tag>content</tag>这种格式的字符串，其中tag可以为aas(amino acid sequence), cds(密码子), ncds(非编码核酸)

```python
from prolm.prolm_tokenizer import ProLMTokenizer
tokenizer = ProLMTokenizer()

# 蛋白质tokenize
protein_sequence = "<aas>MAVFGHVLNM</aas>"
print(tokenizer.tokenize(protein_sequence))
# ['<cls>', 'M', 'A', 'V', 'F', 'G', 'H', 'V', 'L', 'N', 'M', '<sep>']

# 密码子tokenize
cds_sequence = "<cds>ACGCGTACG</cds>"
print(tokenizer.tokenize(cds_sequence))
# ['<cls>', 'acg', 'cgt', 'acg', '<sep>']

# 非编码核酸tokenize
ncds_sequence = "<ncds>ACGCGTACG</ncds>"
print(tokenizer.tokenize(ncds_sequence))
# ['<cls>', 'a', 'c', 'g', 'c', 'g', 't', 'a', 'c', 'g', '<sep>']

# 复合体tokenize
complex_sequence = "<cds>ATCGCT</cds><ncds>atcg</ncds><aas>MAV</aas>"
print(tokenizer.tokenize(complex_sequence))
# ['<cls>', 'atc', 'gct', '<sep>', 'a', 't', 'c', 'g', '<sep>', 'M', 'A', 'V', '<sep>']

# 不添加<cls>和<sep>
ncds_sequence = "<ncds>ACGCGTACG</ncds>"
print(tokenizer.tokenize(ncds_sequence, special_add=None))
# ['a', 'c', 'g', 'c', 'g', 't', 'a', 'c', 'g']

# 添加<bos>和<eos>(decoder专用)
ncds_sequence = "<ncds>ACGCGTACG</ncds>"
print(tokenizer.tokenize(ncds_sequence, special_add="decoder"))
```

### 2. Tokenize and to tensor (输入是列表)
```python
ncds_sequence = ["<ncds>ACGCGTACG</ncds>", ]
print(tokenizer(ncds_sequence, special_add="decoder"))

# {'input_ids': tensor([[ 4, 93, 94, 95, 94, 95, 96, 93, 94, 95,  5]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'length': tensor([11]), 'special_tokens_mask': tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])}
```

### 3. 模型更新(0.0.4)