Revert "feat!: special tokens encoded by default (#512)"

This reverts commit 9da8748.

CHANGELOG.md

@@ -1,12 +1,12 @@
 # Changelog
 
-## v0.21.0
+## v0.20.2
 
-### Breaking Changes
+### What's New
 
-- Special tokens are now also encoded by both Huggingface and Tiktoken tokenizers. This is closer to the default behavior on the Python side, and should make sure if a model adds tokens at the beginning or end of a sequence, these are accounted for as well. This is especially important for embedding models that can add a special token to the beginning of the sequence, and the chunks generated didn't actually fit within the context window because of this.
+#### Python
 
-### What's New
+- Minor release to include latest pyo3 and tree-sitter dependencies.
 
 #### Rust
 

Cargo.toml

@@ -2,7 +2,7 @@
 members = ["bindings/*"]
 
 [workspace.package]
-version = "0.21.0"
+version = "0.20.2"
 authors = ["Ben Brandt <[email protected]>"]
 edition = "2021"
 description = "Split text into semantic chunks, up to a desired chunk size. Supports calculating length by characters and tokens, and is callable from Rust and Python."

bindings/python/tests/test_integration.py

@@ -46,37 +46,37 @@ def test_chunks_trim() -> None:
 
 def test_hugging_face() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
-    splitter = TextSplitter.from_huggingface_tokenizer(tokenizer, 3, trim=False)
+    splitter = TextSplitter.from_huggingface_tokenizer(tokenizer, 1, trim=False)
     text = "123\n123"
     assert splitter.chunks(text) == ["123\n", "123"]
 
 
 def test_hugging_face_range() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
     splitter = TextSplitter.from_huggingface_tokenizer(
-        tokenizer, capacity=(3, 4), trim=False
+        tokenizer, capacity=(1, 2), trim=False
     )
     text = "123\n123"
     assert splitter.chunks(text=text) == ["123\n", "123"]
 
 
 def test_hugging_face_trim() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
-    splitter = TextSplitter.from_huggingface_tokenizer(tokenizer, 2)
+    splitter = TextSplitter.from_huggingface_tokenizer(tokenizer, 1)
     text = "123\n123"
     assert splitter.chunks(text) == ["123", "123"]
 
 
 def test_hugging_face_from_str() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
-    splitter = TextSplitter.from_huggingface_tokenizer_str(tokenizer.to_str(), 2)
+    splitter = TextSplitter.from_huggingface_tokenizer_str(tokenizer.to_str(), 1)
     text = "123\n123"
     assert splitter.chunks(text) == ["123", "123"]
 
 
 def test_hugging_face_from_file() -> None:
     splitter = TextSplitter.from_huggingface_tokenizer_file(
-        "tests/bert-base-cased.json", 2
+        "tests/bert-base-cased.json", 1
     )
     text = "123\n123"
     assert splitter.chunks(text) == ["123", "123"]
@@ -139,39 +139,39 @@ def test_markdown_chunks_trim() -> None:
 
 def test_markdown_hugging_face() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
-    splitter = MarkdownSplitter.from_huggingface_tokenizer(tokenizer, 3, trim=False)
+    splitter = MarkdownSplitter.from_huggingface_tokenizer(tokenizer, 1, trim=False)
     text = "123\n\n123"
     assert splitter.chunks(text) == ["123\n", "\n123"]
 
 
 def test_markdown_hugging_face_range() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
     splitter = MarkdownSplitter.from_huggingface_tokenizer(
-        tokenizer, capacity=(3, 4), trim=False
+        tokenizer, capacity=(1, 2), trim=False
     )
     text = "123\n\n123"
     assert splitter.chunks(text=text) == ["123\n", "\n123"]
 
 
 def test_markdown_hugging_face_trim() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
-    splitter = MarkdownSplitter.from_huggingface_tokenizer(tokenizer, capacity=2)
+    splitter = MarkdownSplitter.from_huggingface_tokenizer(tokenizer, capacity=1)
     text = "123\n\n123"
     assert splitter.chunks(text=text) == ["123", "123"]
 
 
 def test_markdown_hugging_face_from_str() -> None:
     tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
     splitter = MarkdownSplitter.from_huggingface_tokenizer_str(
-        tokenizer.to_str(), capacity=2
+        tokenizer.to_str(), capacity=1
     )
     text = "123\n\n123"
     assert splitter.chunks(text=text) == ["123", "123"]
 
 
 def test_markdown_hugging_face_from_file() -> None:
     splitter = MarkdownSplitter.from_huggingface_tokenizer_file(
-        "tests/bert-base-cased.json", capacity=2
+        "tests/bert-base-cased.json", capacity=1
     )
     text = "123\n\n123"
     assert splitter.chunks(text=text) == ["123", "123"]

src/chunk_size/huggingface.rs

@@ -33,7 +33,7 @@ impl ChunkSizer for &Tokenizer {
     /// encounters text it can't tokenize.
     fn size(&self, chunk: &str) -> usize {
         let encoding = self
-            .encode(chunk, true)
+            .encode(chunk, false)
             .expect("Unable to tokenize the following string {chunk}");
 
         let pad_id = self.get_padding().map(|params| params.pad_id);
@@ -61,8 +61,7 @@ mod tests {
     fn returns_size() {
         let tokenizer = Tokenizer::from_pretrained("bert-base-cased", None).unwrap();
         let size = tokenizer.size(" An apple a");
-        // Bert has a beginning and end token
-        assert_eq!(size, 5);
+        assert_eq!(size, 3);
     }
 
     #[test]
@@ -78,8 +77,7 @@ mod tests {
     fn handles_padding() {
         let tokenizer = Tokenizer::from_pretrained("thenlper/gte-small", None).unwrap();
         let size = tokenizer.size("An apple a");
-        // Has a beginning and end token
-        assert_eq!(size, 5);
+        assert_eq!(size, 3);
     }
 
     #[test]
@@ -89,9 +87,8 @@ mod tests {
 
         // Need to ensure chunk is large enough to cause Encoding overflows.
         assert_eq!(
-            tokenizer.size(" An apple a day keeps the doctor away".repeat(16).as_str()),
-            // Overflows at 128, with special tokens at beginning and end of each section of tokens
-            132
+            tokenizer.size("An apple a day keeps the doctor away.".repeat(100).as_str()),
+            900
         );
     }
 }

src/chunk_size/tiktoken.rs

@@ -5,7 +5,7 @@ use crate::ChunkSizer;
 impl ChunkSizer for &CoreBPE {
     /// Returns the number of tokens in a given text after tokenization.
     fn size(&self, chunk: &str) -> usize {
-        self.encode_with_special_tokens(chunk).len()
+        self.encode_ordinary(chunk).len()
     }
 }