diff --git a/Cargo.toml b/Cargo.toml
index 59c66a9..d9383f3 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "rust-bert"
-version = "0.10.0"
+version = "0.11.0"
 authors = ["Guillaume Becquin <guillaume.becquin@gmail.com>"]
 edition = "2018"
 description = "Ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)"
diff --git a/README.md b/README.md
index 099aaa1..37fe2ef 100644
--- a/README.md
+++ b/README.md
@@ -10,17 +10,17 @@ This repository exposes the model base architecture, task-specific heads (see be
 
 The following models are currently implemented:
 
- | |**DistilBERT**|**BERT**|**RoBERTa**|**GPT**|**GPT2**|**BART**|**Electra**|**Marian**|**ALBERT**|**T5**|
-:-----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:
-Masked LM|✅ |✅ |✅ | | | |✅| |✅ | |
-Sequence classification|✅ |✅ |✅| | |✅ | | |✅ | |
-Token classification|✅ |✅ | ✅| | | |✅| |✅ | |
-Question answering|✅ |✅ |✅| | | | | |✅ | |
-Multiple choices| |✅ |✅| | | | | |✅ | |
-Next token prediction| | | |✅|✅|✅| | | | |
-Natural Language Generation| | | |✅|✅|✅| | | | |
-Summarization | | | | | |✅| | | | |
-Translation | | | | | |✅| |✅ | |✅|
+ | |**DistilBERT**|**BERT**|**RoBERTa**|**GPT**|**GPT2**|**BART**|**Electra**|**Marian**|**ALBERT**|**T5**|**XLNet**|
+:-----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:----:|:----:|:----:|:----:
+Masked LM|✅ |✅ |✅ | | | |✅| |✅ | |✅ |
+Sequence classification|✅ |✅ |✅| | |✅ | | |✅ | |✅ |
+Token classification|✅ |✅ | ✅| | | |✅| |✅ | |✅ |
+Question answering|✅ |✅ |✅| | | | | |✅ | |✅ |
+Multiple choices| |✅ |✅| | | | | |✅ | |✅ |✅ |
+Next token prediction| | | |✅|✅|✅| | | | |✅ |
+Natural Language Generation| | | |✅|✅|✅| | | | |✅ |
+Summarization | | | | | |✅| | | | | |
+Translation | | | | | |✅| |✅ | |✅| |
 
 ## Ready-to-use pipelines
 	
diff --git a/examples/bart.rs b/examples/bart.rs
index 14467f4..83e5dd1 100644
--- a/examples/bart.rs
+++ b/examples/bart.rs
@@ -54,7 +54,7 @@ fn main() -> anyhow::Result<()> {
 
     //    Credits: WikiNews, CC BY 2.5 license (https://en.wikinews.org/wiki/Astronomers_find_water_vapour_in_atmosphere_of_exoplanet_K2-18b)
 
-    let tokenized_input = tokenizer.encode_list(&input, 1024, &TruncationStrategy::LongestFirst, 0);
+    let tokenized_input = tokenizer.encode_list(input, 1024, &TruncationStrategy::LongestFirst, 0);
     let max_len = tokenized_input
         .iter()
         .map(|input| input.token_ids.len())
diff --git a/src/lib.rs b/src/lib.rs
index 1249f36..d95f95a 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -31,17 +31,17 @@
 //! ```
 //! - Transformer models base architectures with customized heads. These allow to load pre-trained models for customized inference in Rust
 //!
-//!  | |**DistilBERT**|**BERT**|**RoBERTa**|**GPT**|**GPT2**|**BART**|**Electra**|**Marian**|**ALBERT**|**T5**
-//! :-----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:
-//! Masked LM|✅ |✅ |✅ | | | |✅| |✅ | |
-//! Sequence classification|✅ |✅ |✅| | |✅| | |✅ | |
-//! Token classification|✅ |✅ | ✅| | | |✅| |✅ | |
-//! Question answering|✅ |✅ |✅| | | | | |✅ | |
-//! Multiple choices| |✅ |✅| | | | | |✅ | |
-//! Next token prediction| | | |✅|✅| | | | | |
-//! Natural Language Generation| | | |✅|✅| | | | | |
-//! Summarization| | | | | |✅| | | | |
-//! Translation| | | | | | | |✅| |✅|
+//!  | |**DistilBERT**|**BERT**|**RoBERTa**|**GPT**|**GPT2**|**BART**|**Electra**|**Marian**|**ALBERT**|**T5**|**XLNet**
+//! :-----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:
+//! Masked LM|✅ |✅ |✅ | | | |✅| |✅ | |✅|
+//! Sequence classification|✅ |✅ |✅| | |✅| | |✅ | |✅|
+//! Token classification|✅ |✅ | ✅| | | |✅| |✅ | |✅|
+//! Question answering|✅ |✅ |✅| | | | | |✅ | |✅|
+//! Multiple choices| |✅ |✅| | | | | |✅ | |✅|
+//! Next token prediction| | | |✅|✅| | | | | |✅|
+//! Natural Language Generation| | | |✅|✅| | | | | |✅|
+//! Summarization| | | | | |✅| | | | | |
+//! Translation| | | | | | | |✅| |✅| |
 //!
 //! # Loading pre-trained models
 //!
diff --git a/src/pipelines/common.rs b/src/pipelines/common.rs
index e8bb61d..a9ca198 100644
--- a/src/pipelines/common.rs
+++ b/src/pipelines/common.rs
@@ -448,13 +448,13 @@ impl TokenizerOption {
     /// Interface method to convert tokens to ids
     pub fn convert_tokens_to_ids(&self, tokens: &[String]) -> Vec<i64> {
         match *self {
-            Self::Bert(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
-            Self::Roberta(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
-            Self::Marian(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
-            Self::T5(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
-            Self::XLMRoberta(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
-            Self::Albert(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
-            Self::XLNet(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens.into()),
+            Self::Bert(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
+            Self::Roberta(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
+            Self::Marian(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
+            Self::T5(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
+            Self::XLMRoberta(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
+            Self::Albert(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
+            Self::XLNet(ref tokenizer) => tokenizer.convert_tokens_to_ids(tokens),
         }
     }
 
diff --git a/src/pipelines/ner.rs b/src/pipelines/ner.rs
index 1cbc696..3f121c0 100644
--- a/src/pipelines/ner.rs
+++ b/src/pipelines/ner.rs
@@ -188,7 +188,10 @@ impl NERModel {
     /// # Ok(())
     /// # }
     /// ```
-    pub fn predict(&self, input: &[&str]) -> Vec<Entity> {
+    pub fn predict<'a, S>(&self, input: S) -> Vec<Entity>
+    where
+        S: AsRef<[&'a str]>,
+    {
         self.token_classification_model
             .predict(input, true, false)
             .into_iter()
diff --git a/src/pipelines/zero_shot_classification.rs b/src/pipelines/zero_shot_classification.rs
index 1c17711..c4d5103 100644
--- a/src/pipelines/zero_shot_classification.rs
+++ b/src/pipelines/zero_shot_classification.rs
@@ -465,22 +465,32 @@ impl ZeroShotClassificationModel {
         })
     }
 
-    fn prepare_for_model(
+    fn prepare_for_model<'a, S, T>(
         &self,
-        inputs: &[&str],
-        labels: &[&str],
+        inputs: S,
+        labels: T,
         template: Option<Box<dyn Fn(&str) -> String>>,
         max_len: usize,
-    ) -> (Tensor, Tensor) {
+    ) -> (Tensor, Tensor)
+    where
+        S: AsRef<[&'a str]>,
+        T: AsRef<[&'a str]>,
+    {
         let label_sentences: Vec<String> = match template {
-            Some(function) => labels.iter().map(|label| function(label)).collect(),
+            Some(function) => labels
+                .as_ref()
+                .iter()
+                .map(|label| function(label))
+                .collect(),
             None => labels
+                .as_ref()
                 .iter()
                 .map(|label| format!("This example is about {}.", label))
                 .collect(),
         };
 
         let text_pair_list = inputs
+            .as_ref()
             .iter()
             .cartesian_product(label_sentences.iter())
             .map(|(&s, label)| (s, label.as_str()))
@@ -577,15 +587,20 @@ impl ZeroShotClassificationModel {
     /// ]
     /// .to_vec();
     /// ```
-    pub fn predict(
+    pub fn predict<'a, S, T>(
         &self,
-        inputs: &[&str],
-        labels: &[&str],
+        inputs: S,
+        labels: T,
         template: Option<Box<dyn Fn(&str) -> String>>,
         max_length: usize,
-    ) -> Vec<Label> {
-        let num_inputs = inputs.len();
-        let (input_tensor, mask) = self.prepare_for_model(inputs, labels, template, max_length);
+    ) -> Vec<Label>
+    where
+        S: AsRef<[&'a str]>,
+        T: AsRef<[&'a str]>,
+    {
+        let num_inputs = inputs.as_ref().len();
+        let (input_tensor, mask) =
+            self.prepare_for_model(inputs.as_ref(), labels.as_ref(), template, max_length);
         let output = no_grad(|| {
             let output = self.zero_shot_classifier.forward_t(
                 Some(input_tensor),
@@ -595,7 +610,7 @@ impl ZeroShotClassificationModel {
                 None,
                 false,
             );
-            output.view((num_inputs as i64, labels.len() as i64, -1i64))
+            output.view((num_inputs as i64, labels.as_ref().len() as i64, -1i64))
         });
 
         let scores = output.softmax(1, Float).select(-1, -1);
@@ -608,7 +623,7 @@ impl ZeroShotClassificationModel {
 
         let mut output_labels: Vec<Label> = vec![];
         for sentence_idx in 0..label_indices.len() {
-            let label_string = labels[label_indices[sentence_idx] as usize].to_string();
+            let label_string = labels.as_ref()[label_indices[sentence_idx] as usize].to_string();
             let label = Label {
                 text: label_string,
                 score: scores[sentence_idx],
@@ -713,15 +728,20 @@ impl ZeroShotClassificationModel {
     /// ]
     /// .to_vec();
     /// ```
-    pub fn predict_multilabel(
+    pub fn predict_multilabel<'a, S, T>(
         &self,
-        inputs: &[&str],
-        labels: &[&str],
+        inputs: S,
+        labels: T,
         template: Option<Box<dyn Fn(&str) -> String>>,
         max_length: usize,
-    ) -> Vec<Vec<Label>> {
-        let num_inputs = inputs.len();
-        let (input_tensor, mask) = self.prepare_for_model(inputs, labels, template, max_length);
+    ) -> Vec<Vec<Label>>
+    where
+        S: AsRef<[&'a str]>,
+        T: AsRef<[&'a str]>,
+    {
+        let num_inputs = inputs.as_ref().len();
+        let (input_tensor, mask) =
+            self.prepare_for_model(inputs.as_ref(), labels.as_ref(), template, max_length);
         let output = no_grad(|| {
             let output = self.zero_shot_classifier.forward_t(
                 Some(input_tensor),
@@ -731,7 +751,7 @@ impl ZeroShotClassificationModel {
                 None,
                 false,
             );
-            output.view((num_inputs as i64, labels.len() as i64, -1i64))
+            output.view((num_inputs as i64, labels.as_ref().len() as i64, -1i64))
         });
         let scores = output.slice(-1, 0, 3, 2).softmax(-1, Float).select(-1, -1);
 
@@ -745,7 +765,7 @@ impl ZeroShotClassificationModel {
                 .collect::<Vec<f64>>();
 
             for (label_index, score) in sentence_scores.into_iter().enumerate() {
-                let label_string = labels[label_index].to_string();
+                let label_string = labels.as_ref()[label_index].to_string();
                 let label = Label {
                     text: label_string,
                     score,