Upload 10 files

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

scripts/gte_embedding.py

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+# coding=utf-8
+# Copyright 2024 The GTE Team Authors and Alibaba Group.
+# Licensed under the Apache License, Version 2.0 (the "License");
+
+from collections import defaultdict
+from typing import Dict, List, Tuple
+
+import numpy as np
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+from transformers.utils import is_torch_npu_available
+
+
+class GTEEmbeddidng(torch.nn.Module):
+    def __init__(self,
+                 model_name: str = None,
+                 normalized: bool = True,
+                 use_fp16: bool = True,
+                 device: str = None
+                ):
+        super().__init__()
+        self.normalized = normalized
+        if device:
+            self.device = torch.device(device)
+        else:
+            if torch.cuda.is_available():
+                self.device = torch.device("cuda")
+            elif torch.backends.mps.is_available():
+                self.device = torch.device("mps")
+            elif is_torch_npu_available():
+                self.device = torch.device("npu")
+            else:
+                self.device = torch.device("cpu")
+                use_fp16 = False
+        self.use_fp16 = use_fp16
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForTokenClassification.from_pretrained(
+            model_name, trust_remote_code=True, torch_dtype=torch.float16 if self.use_fp16 else None
+        )
+        self.vocab_size = self.model.config.vocab_size
+        self.model.to(self.device)
+
+    def _process_token_weights(self, token_weights: np.ndarray, input_ids: list):
+        # conver to dict
+        result = defaultdict(int)
+        unused_tokens = set([self.tokenizer.cls_token_id, self.tokenizer.eos_token_id, self.tokenizer.pad_token_id,
+                             self.tokenizer.unk_token_id])
+        # token_weights = np.ceil(token_weights * 100)
+        for w, idx in zip(token_weights, input_ids):
+            if idx not in unused_tokens and w > 0:
+                token = self.tokenizer.decode([int(idx)])
+                if w > result[token]:
+                    result[token] = w
+        return result
+
+    @torch.no_grad()
+    def encode(self,
+               texts: None,
+               dimension: int = None,
+               max_length: int = 8192,
+               batch_size: int = 16,
+               return_dense: bool = True,
+               return_sparse: bool = False):
+        if dimension is None:
+            dimension = self.model.config.hidden_size
+        if isinstance(texts, str):
+            texts = [texts]
+        num_texts = len(texts)
+        all_dense_vecs = []
+        all_token_weights = []
+        for n, i in enumerate(range(0, num_texts, batch_size)):
+            batch = texts[i: i + batch_size]
+            resulst = self._encode(batch, dimension, max_length, batch_size, return_dense, return_sparse)
+            if return_dense:
+                all_dense_vecs.append(resulst['dense_embeddings'])
+            if return_sparse:
+                all_token_weights.extend(resulst['token_weights'])
+        all_dense_vecs = torch.cat(all_dense_vecs, dim=0)
+        return {
+            "dense_embeddings": all_dense_vecs,
+            "token_weights": all_token_weights 
+        }
+
+    @torch.no_grad()
+    def _encode(self,
+                texts: Dict[str, torch.Tensor] = None,
+                dimension: int = None,
+                max_length: int = 1024,
+                batch_size: int = 16,
+                return_dense: bool = True,
+                return_sparse: bool = False):
+
+        text_input = self.tokenizer(texts, padding=True, truncation=True, return_tensors='pt', max_length=max_length)
+        text_input = {k: v.to(self.model.device) for k,v in text_input.items()}
+        model_out = self.model(**text_input, return_dict=True)
+
+        output = {}
+        if return_dense:
+            dense_vecs = model_out.last_hidden_state[:, 0, :dimension]
+            if self.normalized:
+                dense_vecs = torch.nn.functional.normalize(dense_vecs, dim=-1)
+            output['dense_embeddings'] = dense_vecs
+        if return_sparse:
+            token_weights = torch.relu(model_out.logits).squeeze(-1)
+            token_weights = list(map(self._process_token_weights, token_weights.detach().cpu().numpy().tolist(),
+                                                    text_input['input_ids'].cpu().numpy().tolist()))
+            output['token_weights'] = token_weights
+
+        return output
+
+    def _compute_sparse_scores(self, embs1, embs2):
+        scores = 0
+        for token, weight in embs1.items():
+            if token in embs2:
+                scores += weight * embs2[token]
+        return scores
+
+    def compute_sparse_scores(self, embs1, embs2):
+        scores = [self._compute_sparse_scores(emb1, emb2) for emb1, emb2 in zip(embs1, embs2)]
+        return np.array(scores)
+
+    def compute_dense_scores(self, embs1, embs2):
+        scores = torch.sum(embs1*embs2, dim=-1).cpu().detach().numpy()
+        return scores
+
+    @torch.no_grad()
+    def compute_scores(self, 
+        text_pairs: List[Tuple[str, str]], 
+        dimension: int = None,
+        max_length: int = 1024,
+        batch_size: int = 16,
+        dense_weight=1.0,
+        sparse_weight=0.1):
+        text1_list = [text_pair[0] for text_pair in text_pairs]
+        text2_list = [text_pair[1] for text_pair in text_pairs]
+        embs1 = self.encode(text1_list, dimension, max_length, batch_size, return_dense=True, return_sparse=True)
+        embs2 = self.encode(text2_list, dimension, max_length, batch_size, return_dense=True, return_sparse=True)
+        scores = self.compute_dense_scores(embs1['dense_embeddings'], embs2['dense_embeddings']) * dense_weight + \
+            self.compute_sparse_scores(embs1['token_weights'], embs2['token_weights']) * sparse_weight
+        scores = scores.tolist()
+        return scores
+
+
+if __name__ == '__main__':
+    gte = GTEEmbeddidng('Alibaba-NLP/gte-multilingual-base')
+    docs =  [
+        "黑龙江离俄罗斯很近",
+        "哈尔滨是中国黑龙江省的省会，位于中国东北",
+        "you are the hero"
+    ]
+    print('docs', docs)
+    embs = gte.encode(docs, return_dense=True,return_sparse=True)
+    print('dense vecs', embs['dense_embeddings'])
+    print('sparse vecs', embs['token_weights'])

sentence_bert_config.json

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+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}