Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+---
+# PubMedBERT Embeddings Matryoshka - ONNX - O4
+
+O4 optimized weights of [`NeuML/pubmedbert-base-embeddings-matryoshka`](https://huggingface.co/NeuML/pubmedbert-base-embeddings-matryoshka). 
+
+```python
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from transformers import AutoTokenizer
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def meanpooling(output, mask):
+    embeddings = output[0] # First element of model_output contains all token embeddings
+    mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
+    return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+model = ORTModelForFeatureExtraction.from_pretrained("hooman650/pubmedbert-base-embeddings-matryoshka-onnx-04",provider="CUDAExecutionProvider")
+tokenizer = AutoTokenizer.from_pretrained("hooman650/pubmedbert-base-embeddings-matryoshka-onnx-04")
+
+# Tokenize sentences
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    output = model(**inputs)
+
+# Perform pooling. In this case, mean pooling.
+embeddings = meanpooling(output, inputs['attention_mask'])
+
+# Requested matryoshka dimensions
+dimensions = 256
+
+print("Sentence embeddings:")
+print(embeddings[:, :dimensions])