Create README.md

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- transformers
+- Qwen2
+license: other
+license_name: neoai-open-rail-m
+license_link: LICENSE
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
+---
+
+
+
+
+## NeoAI-Embed 
+**NeoAI-Embed is a state-of-the-art** code embedding model designed for retrieval tasks in the software development domain.
+It is offered in two sizes: lite (1.5B) and medium (7B). The model is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search, retrieval-augmented generation (RAG), and contextual understanding of programming languages.
+This model outperforms all previous open-source models in the COIR and MTEB leaderboards, achieving best-in-class performance with a significantly smaller size compared to competing models.
+
+### Languages Supported: 
+* Python
+* C++
+* C#
+* Go
+* Java
+* Javascript
+* PHP
+* Ruby
+* Typescript
+
+
+## Model Information
+- Model Size: 1.5B 
+- Embedding Dimension: 1536
+- Max Input Tokens: 32k
+
+## Requirements
+```
+transformers>=4.39.2
+flash_attn>=2.5.6
+```
+
+## Usage
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("khulnasoft/NeoAI-Embed")
+# Run inference
+sentences = [
+    'accumulator = sum(item.value for item in collection)',  
+    'result = reduce(lambda acc, curr: acc + curr.amount, data, 0)',  
+    'matrix = [[i*j for j in range(n)] for i in range(n)]'  
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1536]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+### Transformers
+
+```python
+import torch
+import torch.nn.functional as F
+
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+
+def last_token_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+
+# Each query must come with a one-sentence instruction that describes the task
+queries = [
+      'how to handle memory efficient data streaming',
+      'implement binary tree traversal'
+  ]
+
+documents = [
+        """def process_in_chunks():
+            buffer = deque(maxlen=1000)
+            for record in source_iterator:
+                buffer.append(transform(record))
+                if len(buffer) >= 1000:
+                    yield from buffer
+                    buffer.clear()""",
+
+        """class LazyLoader:
+            def __init__(self, source):
+                self.generator = iter(source)
+                self._cache = []
+
+            def next_batch(self, size=100):
+                while len(self._cache) < size:
+                    try:
+                        self._cache.append(next(self.generator))
+                    except StopIteration:
+                        break
+                return self._cache.pop(0) if self._cache else None""",
+
+        """def dfs_recursive(root):
+            if not root:
+                return []
+            stack = []
+            stack.extend(dfs_recursive(root.right))
+            stack.append(root.val)
+            stack.extend(dfs_recursive(root.left))
+            return stack"""
+    ]
+input_texts = queries + documents
+
+tokenizer = AutoTokenizer.from_pretrained('khulnasoft/NeoAI-Embed', trust_remote_code=True)
+model = AutoModel.from_pretrained('khulnasoft/NeoAI-Embed', trust_remote_code=True)
+
+max_length = 8192
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
+outputs = model(**batch_dict)
+embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+```
+
+
+
+
+## License
+[NeoAI-Open-RAIL-M](https://www.neoai.khulnasoft.com/open-rail-m-license/)
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