chore: Miscellaneous Code Improvements

.releaserc

@@ -64,7 +64,6 @@
         ]
       }
     ],
-    "@semantic-release/release-notes-generator",
     "@semantic-release/github",
     [
       "semantic-release-cargo",
@@ -108,10 +107,6 @@
               "type": "feat",
               "section": "🍕 Features"
             },
-            {
-              "type": "feature",
-              "section": "🍕 Features"
-            },
             {
               "type": "fix",
               "section": "🐛 Bug Fixes"

src/common.rs

@@ -97,7 +97,7 @@ pub fn load_tokenizer(tokenizer_files: TokenizerFiles, max_length: usize) -> Res
 
     let mut tokenizer = tokenizer
         .with_padding(Some(PaddingParams {
-            // TODO: the user should able to choose the padding strategy
+            // TODO: the user should be able to choose the padding strategy
             strategy: PaddingStrategy::BatchLongest,
             pad_token,
             pad_id,

src/image_embedding/utils.rs

@@ -156,7 +156,7 @@ impl Transform for Normalize {
                 Ok(TransformData::NdArray(array_normalized))
             }
             _ => Err(anyhow!(
-                "Transformer convert error. Normlize operator get error shape."
+                "Transformer convert error. Normalize operator got error shape."
             )),
         }
     }
@@ -236,11 +236,11 @@ fn load_preprocessor(config: serde_json::Value) -> anyhow::Result<Compose> {
                         crop_size["height"]
                             .as_u64()
                             .map(|height| height as u32)
-                            .ok_or(anyhow!("crop_size height must be cotained"))?,
+                            .ok_or(anyhow!("crop_size height must be contained"))?,
                         crop_size["width"]
                             .as_u64()
                             .map(|width| width as u32)
-                            .ok_or(anyhow!("crop_size width must be cotained"))?,
+                            .ok_or(anyhow!("crop_size width must be contained"))?,
                     )
                 } else {
                     return Err(anyhow!("Invalid crop size: {:?}", crop_size));
@@ -325,7 +325,7 @@ fn load_preprocessor(config: serde_json::Value) -> anyhow::Result<Compose> {
                 }));
             }
         }
-        mode => return Err(anyhow!("Preprocessror {} is not supported", mode)),
+        mode => return Err(anyhow!("Preprocessor {} is not supported", mode)),
     }
 
     transformers.push(Box::new(PILToNDarray));

src/init.rs

@@ -49,17 +49,17 @@ impl<M: Default> Default for InitOptions<M> {
 }
 
 impl<M: Default + HasMaxLength> InitOptionsWithLength<M> {
-    /// Crea a new InitOptionsWithLength with the given model name
+    /// Create a new InitOptionsWithLength with the given model name
     pub fn new(model_name: M) -> Self {
         Self {
             model_name,
             ..Default::default()
         }
     }
 
-    /// Set the maximum maximum length
-    pub fn with_max_length(mut self, max_lenght: usize) -> Self {
-        self.max_length = max_lenght;
+    /// Set the maximum length
+    pub fn with_max_length(mut self, max_length: usize) -> Self {
+        self.max_length = max_length;
         self
     }
 
@@ -86,7 +86,7 @@ impl<M: Default + HasMaxLength> InitOptionsWithLength<M> {
 }
 
 impl<M: Default> InitOptions<M> {
-    /// Crea a new InitOptionsWithLength with the given model name
+    /// Create a new InitOptions with the given model name
     pub fn new(model_name: M) -> Self {
         Self {
             model_name,

src/reranking/impl.rs

@@ -67,7 +67,7 @@ impl TextRerank {
         let api = ApiBuilder::from_cache(cache)
             .with_progress(show_download_progress)
             .build()
-            .expect("Failed to build API from cache");
+            .map_err(|e| anyhow::Error::msg(format!("Failed to build API from cache: {}", e)))?;
         let model_repo = api.model(model_name.to_string());
 
         let model_file_name = TextRerank::get_model_info(&model_name).model_file;
@@ -138,7 +138,7 @@ impl TextRerank {
             let encodings = self
                 .tokenizer
                 .encode_batch(inputs, true)
-                .expect("Failed to encode batch");
+                .map_err(|e| anyhow::Error::msg(e.to_string()).context("Failed to encode batch"))?;
 
             let encoding_length = encodings[0].len();
             let batch_size = batch.len();
@@ -176,9 +176,13 @@ impl TextRerank {
             }
 
             let outputs = self.session.run(session_inputs)?;
-            let outputs = outputs["logits"]
+            let outputs = outputs
+                .get("logits")
+                .ok_or_else(|| anyhow::Error::msg("Output does not contain 'logits' key"))?
                 .try_extract_array()
-                .expect("Failed to extract logits tensor");
+                .map_err(|e| {
+                    anyhow::Error::msg(format!("Failed to extract logits tensor: {}", e))
+                })?;
             let batch_scores: Vec<f32> = outputs
                 .slice(s![.., 0])
                 .rows()
@@ -199,6 +203,6 @@ impl TextRerank {
             })
             .collect();
         top_n_result.sort_by(|a, b| a.score.total_cmp(&b.score).reverse());
-        Ok(top_n_result.to_vec())
+        Ok(top_n_result)
     }
 }

src/text_embedding/output.rs

@@ -19,7 +19,7 @@ pub const OUTPUT_TYPE_PRECEDENCE: &[OutputKey] = &[
     // OutputKey::ByName("token_embeddings"),
 ];
 
-/// Generates thea default array transformer for the [`TextEmbedding`] model using the
+/// Generates the default array transformer for the [`TextEmbedding`] model using the
 /// provided output precedence.
 ///
 // TODO (denwong47): now that pooling is done in SingleBatchOutput, it is possible that