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_sbert.py
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_sbert.py
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import numpy as np
import pandas as pd
from typing import List, Union
from sentence_transformers import SentenceTransformer
from ._utils import cosine_similarity
from ._base import BaseMatcher
class SentenceEmbeddings(BaseMatcher):
"""
Embed words into vectors and use cosine similarity to find
the best matches between two lists of strings
Arguments:
embedding_model: The sbert model to use, this can be either a string or the model directly
min_similarity: The minimum similarity between strings, otherwise return 0 similarity
top_n: The number of best matches you want returned
cosine_method: The method/package for calculating the cosine similarity.
Options: "sparse", "sklearn", "knn".
Sparse is the fastest and most memory efficient but requires a
package that might be difficult to install.
Sklearn is a bit slower than sparse and requires significantly more memory as
the distance matrix is not sparse
Knn uses 1-nearest neighbor to extract the most similar strings
it is significantly slower than both methods but requires little memory
model_id: The name of the particular instance, used when comparing models
Usage:
```python
distance_model = SentenceEmbeddings("all-MiniLM-L6-v2", min_similarity=0.5)
```
Or if you want to directly pass a sbert model:
```python
from sentence_transformers import SentenceTransformer
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
distance_model = SentenceEmbeddings(embedding_model, min_similarity=0.5)
```
"""
def __init__(self,
embedding_model: Union[str, SentenceTransformer] = "all-MiniLM-L6-v2",
min_similarity: float = 0.75,
top_n: int = 1,
cosine_method: str = "sparse",
model_id: str = None):
super().__init__(model_id)
self.type = "Embeddings"
if isinstance(embedding_model, SentenceTransformer):
self.embedding_model = embedding_model
elif isinstance(embedding_model, str):
self.embedding_model = SentenceTransformer(embedding_model)
else:
raise ValueError("Please select a correct SentenceTransformers model: \n"
"`from sentence_transformers import SentenceTransformer` \n"
"`embedding_model = SentenceTransformer('all-MiniLM-L6-v2')`")
self.min_similarity = min_similarity
self.top_n = top_n
self.cosine_method = cosine_method
self.embeddings_to = None
def match(self,
from_list: List[str],
to_list: List[str] = None,
embeddings_from: np.ndarray = None,
embeddings_to: np.ndarray = None,
re_train: bool = True) -> pd.DataFrame:
""" Matches the two lists of strings to each other and returns the best mapping
Arguments:
from_list: The list from which you want mappings
to_list: The list where you want to map to
embeddings_from: Embeddings you created yourself from the `from_list`
embeddings_to: Embeddings you created yourself from the `to_list`
re_train: Whether to re-train the model with new embeddings
Set this to False if you want to use this model in production
Returns:
matches: The best matches between the lists of strings
Usage:
```python
model = Embeddings(min_similarity=0.5)
matches = model.match(["string_one", "string_two"],
["string_three", "string_four"])
```
"""
# Extract embeddings from the `from_list`
embeddings_from = self.embedding_model.encode(from_list, show_progress_bar=False)
# Extract embeddings from the `to_list` if it exists
if not isinstance(embeddings_to, np.ndarray):
if not re_train:
embeddings_to = self.embeddings_to
elif to_list is None:
embeddings_to = self.embedding_model.encode(from_list, show_progress_bar=False)
else:
embeddings_to = self.embedding_model.encode(to_list, show_progress_bar=False)
matches = cosine_similarity(embeddings_from, embeddings_to,
from_list, to_list,
self.min_similarity,
top_n=self.top_n,
method=self.cosine_method)
self.embeddings_to = embeddings_to
return matches