diff --git a/candle-examples/examples/debertav2/main.rs b/candle-examples/examples/debertav2/main.rs
index 57742b8192..b332813abc 100644
--- a/candle-examples/examples/debertav2/main.rs
+++ b/candle-examples/examples/debertav2/main.rs
@@ -20,14 +20,14 @@ use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::{Encoding, PaddingParams, Tokenizer};
 
 enum TaskType {
-    NER(DebertaV2NERModel),
+    Ner(DebertaV2NERModel),
     TextClassification(DebertaV2SeqClassificationModel),
 }
 
 #[derive(Parser, Debug, Clone, ValueEnum)]
 enum ArgsTask {
     /// Named Entity Recognition
-    NER,
+    Ner,
 
     /// Text Classification
     TextClassification,
@@ -36,7 +36,7 @@ enum ArgsTask {
 impl Display for ArgsTask {
     fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
         match self {
-            ArgsTask::NER => write!(f, "ner"),
+            ArgsTask::Ner => write!(f, "ner"),
             ArgsTask::TextClassification => write!(f, "text-classification"),
         }
     }
@@ -77,7 +77,7 @@ struct Args {
     benchmark_iters: Option<usize>,
 
     /// Which task to run
-    #[arg(long, default_value_t = ArgsTask::NER)]
+    #[arg(long, default_value_t = ArgsTask::Ner)]
     task: ArgsTask,
 
     /// Use model from a specific directory instead of HuggingFace local cache.
@@ -142,7 +142,7 @@ impl Args {
         // Command-line id2label takes precedence. Otherwise, use model config's id2label.
         // If neither is specified, then we can't proceed.
         let id2label = if let Some(id2labelstr) = &self.id2label {
-            serde_json::from_str(&&id2labelstr.as_str())?
+            serde_json::from_str(id2labelstr.as_str())?
         } else if let Some(id2label) = &config.id2label {
             id2label.clone()
         } else {
@@ -174,8 +174,8 @@ impl Args {
         let vb = vb.set_prefix("deberta");
 
         match self.task {
-            ArgsTask::NER => Ok((
-                TaskType::NER(DebertaV2NERModel::load(
+            ArgsTask::Ner => Ok((
+                TaskType::Ner(DebertaV2NERModel::load(
                     vb,
                     &config,
                     Some(id2label.clone()),
@@ -200,7 +200,7 @@ impl Args {
 
 fn get_device(model_type: &TaskType) -> &Device {
     match model_type {
-        TaskType::NER(ner_model) => &ner_model.device,
+        TaskType::Ner(ner_model) => &ner_model.device,
         TaskType::TextClassification(classification_model) => &classification_model.device,
     }
 }
@@ -253,9 +253,9 @@ fn main() -> Result<()> {
         let mut token_type_id_stack: Vec<Tensor> = Vec::default();
 
         for encoding in &tokenizer_encodings {
-            encoding_stack.push(Tensor::new(encoding.get_ids(), &device)?);
-            attention_mask_stack.push(Tensor::new(encoding.get_attention_mask(), &device)?);
-            token_type_id_stack.push(Tensor::new(encoding.get_type_ids(), &device)?);
+            encoding_stack.push(Tensor::new(encoding.get_ids(), device)?);
+            attention_mask_stack.push(Tensor::new(encoding.get_attention_mask(), device)?);
+            token_type_id_stack.push(Tensor::new(encoding.get_type_ids(), device)?);
         }
 
         ModelInput {
@@ -272,7 +272,7 @@ fn main() -> Result<()> {
     );
 
     match task_type {
-        TaskType::NER(ner_model) => {
+        TaskType::Ner(ner_model) => {
             if let Some(num_iters) = args.benchmark_iters {
                 create_benchmark(num_iters, model_input)(
                     |input_ids, token_type_ids, attention_mask| {
@@ -326,7 +326,7 @@ fn main() -> Result<()> {
                     current_row_result.push(NERItem {
                         entity: label,
                         word: current_row_tokens[input_id_idx].clone(),
-                        score: current_row_max_scores[input_id_idx].clone(),
+                        score: current_row_max_scores[input_id_idx],
                         start: current_row_encoding.get_offsets()[input_id_idx].0,
                         end: current_row_encoding.get_offsets()[input_id_idx].1,
                         index: input_id_idx,