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| Property | Value |
|---|---|
| Original Model | sentence-transformers/all-MiniLM-L6-v2 |
| Format | LiteRT (.tflite) |
| File Size | 85.8 MB |
| Task | Sentence Embeddings / Semantic Similarity |
| Max Sequence Length | 128 |
| Output Dimension | 384 |
| Pooling Mode | Mean Pooling |
| Metric | Value |
|---|---|
| Inference Latency | 10.6 ms |
| Throughput | 94.7/sec |
| Cosine Similarity vs Original | 1.0000 ✅ |
1import numpy as np
2from ai_edge_litert.interpreter import Interpreter
3from transformers import AutoTokenizer
4
5# Load model and tokenizer
6interpreter = Interpreter(model_path="sentence-transformers_all-MiniLM-L6-v2.tflite")
7interpreter.allocate_tensors()
8input_details = interpreter.get_input_details()
9output_details = interpreter.get_output_details()
10
11tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
12
13def get_embedding(text: str) -> np.ndarray:
14 """Get sentence embedding for input text."""
15 encoded = tokenizer(
16 text,
17 padding="max_length",
18 max_length=128,
19 truncation=True,
20 return_tensors="np"
21 )
22
23 interpreter.set_tensor(input_details[0]["index"], encoded["input_ids"].astype(np.int64))
24 interpreter.set_tensor(input_details[1]["index"], encoded["attention_mask"].astype(np.int64))
25 interpreter.invoke()
26
27 return interpreter.get_tensor(output_details[0]["index"])[0]
28
29# Example
30embedding = get_embedding("Hello, world!")
31print(f"Embedding shape: {embedding.shape}") # (384,)sentence-transformers_all-MiniLM-L6-v2.tflite - The LiteRT model file1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "EMNLP 2019",
5 year = "2019",
6 url = "https://arxiv.org/abs/1908.10084",
7}