Deprecated use_ann

Author: Pringled

README.md

@@ -318,21 +318,23 @@ deduplicated_texts = semhash.self_deduplicate()
 <summary>  Using custom ANN backends </summary>
 <br>
 
-The following code snippet shows how to use a custom ANN backend and custom args with SemHash:
+By default, we use [USearch](https://github.com/unum-cloud/USearch) as the ANN (approximate-nearest neighbors) backend for deduplication. We recommend keeping this since the recall for smaller datasets is ~100%, and it's needed for larger datasets (>1M samples) since these will take too long to deduplicate without ANN. If you want to use a flat/exact-matching backend, you can set `ann_backend=Backend.BASIC` in the SemHash constructor:
 
 ```python
-from datasets import load_dataset
 from semhash import SemHash
 from vicinity import Backend
 
-# Load a dataset to deduplicate
-texts = load_dataset("ag_news", split="train")["text"]
+semhash = SemHash.from_records(records=texts, ann_backend=Backend.BASIC)
+```
 
-# Initialize a SemHash with the model and custom ann backend and custom args
-semhash = SemHash.from_records(records=texts, ann_backend=Backend.FAISS, nlist=50)
+Any backend from [Vicinity](https://github.com/MinishLab/vicinity) can be used with SemHash. The following code snippet shows how to use [FAISS](https://github.com/facebookresearch/faiss) with a custom `nlist` parameter:
 
-# Deduplicate the texts
-deduplicated_texts = semhash.self_deduplicate()
+```python
+from datasets import load_dataset
+from semhash import SemHash
+from vicinity import Backend
+
+semhash = SemHash.from_records(records=texts, ann_backend=Backend.FAISS, nlist=50)
 ```
 
 For the full list of supported ANN backends and args, see the [Vicinity docs](https://github.com/MinishLab/vicinity/tree/main?tab=readme-ov-file#supported-backends).
@@ -398,13 +400,6 @@ representative_texts = semhash.self_find_representative().selected
 ```
 </details>
 
-NOTE: By default, we use the ANN (approximate-nearest neighbors) backend for deduplication. We recommend keeping this since the recall for smaller datasets is ~100%, and it's needed for larger datasets (>1M samples) since these will take too long to deduplicate without ANN. If you want to use the flat/exact-matching backend, you can set `ann_backend=Backend.BASIC` in the SemHash constructor:
-
-```python
-from vicinity import Backend
-
-semhash = SemHash.from_records(records=texts, ann_backend=Backend.BASIC)
-```