如何获得 BioBERT 嵌入

Question

我在 pandas 数据框中有一个字段，其中有一个文本字段，我想为其生成 BioBERT 嵌入。有没有一种简单的方法可以生成向量嵌入？我想在另一个模型中使用它们。

这是数据框的假设样本

Visit Code	Problem Assessment
1234	ge reflux working diagnosis well
4567	medication refill order working diagnosis note called in brand benicar 5mg qd 30 prn refill

我试过这个包，但安装时收到错误 https://pypi.org/project/biobert-embedding

错误：

Collecting biobert-embedding
  Using cached biobert-embedding-0.1.2.tar.gz (4.8 kB)
ERROR: Could not find a version that satisfies the requirement torch==1.2.0 (from biobert-embedding) (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2, 1.7.1)
ERROR: No matching distribution found for torch==1.2.0 (from biobert-embedding)

非常感谢任何帮助！

Answer 1

尝试如下安装：

pip install biobert-embedding==0.1.2 torch==1.2.0 -f https://download.pytorch.org/whl/torch_stable.html

我扩展了您的示例数据框，以说明您现在如何计算 problem assessments 的句子向量，并使用这些来计算相似 visit codes 之间的余弦相似度。

>>> from biobert_embedding.embedding import BiobertEmbedding
>>> from scipy.spatial import distance
>>> import pandas as pd

>>> data = {'Visit Code': [1234, 1235, 4567, 4568], 
        'Problem Assessment': ['ge reflux working diagnosis well', 
                               'other reflux diagnosis poor', 
                               'medication refill order working diagnosis note called in brand benicar 5mg qd 30 prn refill',
                               'medication must be refilled diagnosis note called in brand Olmesartan 10mg qd 40 prn refill']}

>>> df = pd.DataFrame(data)
>>> df

	Visit Code	Problem Assessment
0	1234	ge reflux working diagnosis well
1	1234	other reflux diagnosis poor
2	4567	medication refill order working diagnosis note called in brand benicar 5mg qd 30 prn refill
3	4567	medication must be refilled diagnosis note called in brand Olmesartan 10mg qd 40 prn refill

>>> biobert = BiobertEmbedding()
>>> df['sentence embedding'] = df['Problem Assessment'].apply(lambda sentence: biobert.sentence_vector(sentence))
>>> df

	Visit Code	Problem Assessment	sentence embedding
0	1234	ge reflux working diagnosis well	tensor([ 2.7189e-01, -1.6195e-01, 5.8270e-02, -3.2730e-01, 7.5583e-02, ...
1	1234	other reflux diagnosis poor	tensor([ 1.6971e-01, -2.1405e-01, 3.4427e-02, -2.3090e-01, 1.6007e-02, ...
2	4567	medication refill order working diagnosis note called in brand benicar 5mg qd 30 prn refill	tensor([ 1.5370e-01, -3.9875e-01, 2.0089e-01, 4.1506e-02, 6.9854e-02, ...
3	4567	medication must be refilled diagnosis note called in brand Olmesartan 10mg qd 40 prn refill	tensor([ 2.2128e-01, -2.0283e-01, 2.2194e-01, 9.1156e-02, 1.1620e-01, ...

>>> df.groupby('Visit Code')['sentence embedding'].apply(lambda sentences: 1 - distance.cosine(sentences.values) )


Visit Code
1234    0.950492
4567    0.969715
Name: sentence embedding, dtype: float64

我们可以看到，不出所料，相似的句子非常接近