Create README.md

README.md

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+---
+tags:
+- fuzzy-matching
+- fuzzy-search
+- entity-resolution
+- record-linking
+- structured-data-search
+---
+A Siamese BERT architecture trained at character levels tokens for embedding based Fuzzy matching.
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+from torch import Tensor, device
+
+def cos_sim(a: Tensor, b: Tensor):
+    """
+    borrowed from sentence transformers repo
+    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.
+    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])
+    """
+    if not isinstance(a, torch.Tensor):
+        a = torch.tensor(a)
+
+    if not isinstance(b, torch.Tensor):
+        b = torch.tensor(b)
+
+    if len(a.shape) == 1:
+        a = a.unsqueeze(0)
+
+    if len(b.shape) == 1:
+        b = b.unsqueeze(0)
+
+    a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
+    b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
+    return torch.mm(a_norm, b_norm.transpose(0, 1))
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Words we want fuzzy embeddings for
+word1 = "fuzzformer"
+word1 = " ".join([char for char in word1]) ## divide the word to char level to fuzzy match
+word2 = "fizzformer"
+word2 = " ".join([char for char in word2]) ## divide the word to char level to fuzzy match
+words = [word1, word2]
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('shahrukhx01/Fuzzformer')
+model = AutoModel.from_pretrained('shahrukhx01/Fuzzformer')
+
+# Tokenize sentences
+encoded_input = tokenizer(words, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, max pooling.
+fuzzy_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Fuzzy Match score:")
+print(cos_sim(fuzzy_embeddings[0], fuzzy_embeddings[1]))
+```
+
+## ACKNOWLEDGEMENT
+A big thank you to [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) as their implementation really expedited the implementation of Fuzzformer.