初始化项目，由ModelHub XC社区提供模型

Model: BrandonZYW/llama-2-7b-InBedder
Source: Original Platform

README.md

@@ -0,0 +1,84 @@
+---
+license: mit
+datasets:
+- KomeijiForce/Inbedder-Pretrain-Data
+language:
+- en
+---
+
+# [ACL2024] Answer is All You Need: Instruction-following Text Embedding via Answering the Question
+
+InBedder🛌 is a text embedder that is designed to follow instructions. Instruction-following text embedder can capture characteristics of texts specified by user instructions. InBedder offers a novel viewpoint that treats the instruction as a question about the input text and encodes the expected answers to obtain the representation accordingly. We show that InBedder is aware of instructions with different evaluation tasks.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64323dd503d81fa4d26deaf9/jLbqF-2uT8Aw9DsN7XCVG.png)
+
+The following is a use case from [https://github.com/zhang-yu-wei/InBedder/blob/main/UseCase.ipynb](https://github.com/zhang-yu-wei/InBedder/blob/main/UseCase.ipynb)
+
+```python
+import torch
+from torch import nn
+from torch.nn.functional import gelu, cosine_similarity
+from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM
+
+import numpy as np
+
+class InBedder():
+    
+    def __init__(self, path='KomeijiForce/inbedder-roberta-large', device='cuda:0'):
+        
+        model = AutoModelForMaskedLM.from_pretrained(path)
+    
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = model.roberta
+        self.dense = model.lm_head.dense
+        self.layer_norm = model.lm_head.layer_norm
+        
+        self.device = torch.device(device)
+        self.model = self.model.to(self.device)
+        self.dense = self.dense.to(self.device)
+        self.layer_norm = self.layer_norm.to(self.device)
+        
+        self.vocab = self.tokenizer.get_vocab()
+        self.vocab = {self.vocab[key]:key for key in self.vocab}
+        
+    def encode(self, input_texts, instruction, n_mask):
+        
+        if type(instruction) == str:
+            prompts = [instruction + self.tokenizer.mask_token*n_mask for input_text in input_texts]
+        elif type(instruction) == list:
+            prompts = [inst + self.tokenizer.mask_token*n_mask for inst in instruction]
+    
+        inputs = self.tokenizer(input_texts, prompts, padding=True, truncation=True, return_tensors='pt').to(self.device)
+
+        mask = inputs.input_ids.eq(self.tokenizer.mask_token_id)
+        
+        outputs = self.model(**inputs)
+
+        logits = outputs.last_hidden_state[mask]
+        
+        logits = self.layer_norm(gelu(self.dense(logits)))
+        
+        logits = logits.reshape(len(input_texts), n_mask, -1)
+        
+        logits = logits.mean(1)
+            
+        logits = (logits - logits.mean(1, keepdim=True)) / logits.std(1, keepdim=True)
+        
+        return logits
+
+inbedder = InBedder(path='KomeijiForce/inbedder-roberta-large', device='cpu')
+
+texts = ["I love cat!", "I love dog!", "I dislike cat!"]
+instruction = "What is the animal mentioned here?"
+embeddings = inbedder.encode(texts, instruction, 3)
+
+cosine_similarity(embeddings[:1], embeddings[1:], dim=1)
+# tensor([0.9374, 0.9917], grad_fn=<SumBackward1>)
+
+texts = ["I love cat!", "I love dog!", "I dislike cat!"]
+instruction = "What is emotion expressed here?"
+embeddings = inbedder.encode(texts, instruction, 3)
+
+cosine_similarity(embeddings[:1], embeddings[1:], dim=1)
+# tensor([0.9859, 0.8537], grad_fn=<SumBackward1>)
+```