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README.md

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+---
+tags:
+- litert
+- tflite
+- on-device
+- mobile-bert
+license: apache-2.0
+base_model: squeezebert/squeezebert-uncased
+---
+
+# squeezebert/squeezebert-uncased - LiteRT Optimized
+
+This is a [LiteRT](https://ai.google.dev/edge/litert) (formerly TensorFlow Lite) export of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased).
+
+It is optimized for mobile and edge inference (Android/iOS/Embedded).
+
+## Model Details
+| Attribute | Value |
+| :--- | :--- |
+| **Task** | Feature Extraction |
+| **Format** | `.tflite` (Float32) |
+| **File Size** | 192.0 MB |
+| **Input Length** | 128 tokens |
+| **Output Dim** | 768 |
+
+## Usage
+
+```python
+import numpy as np
+from ai_edge_litert.interpreter import Interpreter
+from transformers import AutoTokenizer
+
+# Load model
+interpreter = Interpreter(model_path="squeezebert_squeezebert-uncased.tflite")
+interpreter.allocate_tensors()
+
+tokenizer = AutoTokenizer.from_pretrained("squeezebert/squeezebert-uncased")
+
+def get_embedding(text):
+    inputs = tokenizer(text, max_length=128, padding="max_length", truncation=True, return_tensors="np")
+
+    input_details = interpreter.get_input_details()
+    interpreter.set_tensor(input_details[0]['index'], inputs['input_ids'].astype(np.int64))
+    interpreter.set_tensor(input_details[1]['index'], inputs['attention_mask'].astype(np.int64))
+
+    interpreter.invoke()
+
+    output_details = interpreter.get_output_details()
+    return interpreter.get_tensor(output_details[0]['index'])[0]
+
+emb = get_embedding("Hello world")
+print(f"Embedding shape: {emb.shape}")
+```
+
+*Converted by [Bombek1](https://huggingface.co/Bombek1) using [litert-torch](https://github.com/google-ai-edge/ai-edge-torch)*