diff --git a/candle-examples/examples/bert/main.rs b/candle-examples/examples/bert/main.rs
index 58f42f38..bfa4e69a 100644
--- a/candle-examples/examples/bert/main.rs
+++ b/candle-examples/examples/bert/main.rs
@@ -1,7 +1,9 @@
 #![allow(dead_code)]
+// The tokenizer.json and weights should be retrieved from:
+// https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
 
-use anyhow::Error as E;
-use candle::{safetensors::SafeTensors, DType, Device, Result, Shape, Tensor};
+use anyhow::{Error as E, Result};
+use candle::{safetensors::SafeTensors, DType, Device, Shape, Tensor};
 use clap::Parser;
 use std::collections::HashMap;
 
@@ -40,7 +42,7 @@ impl<'a> VarBuilder<'a> {
         }
     }
 
-    pub fn get<S: Into<Shape>>(&self, s: S, tensor_name: &str) -> Result<Tensor> {
+    pub fn get<S: Into<Shape>>(&self, s: S, tensor_name: &str) -> candle::Result<Tensor> {
         let s: Shape = s.into();
         match &self.safetensors {
             None => Tensor::zeros(s, self.dtype, &self.device),
@@ -71,7 +73,7 @@ enum HiddenAct {
 }
 
 impl HiddenAct {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+    fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> {
         match self {
             Self::Gelu => xs.gelu(),
             Self::Relu => xs.relu(),
@@ -164,7 +166,8 @@ impl Embedding {
     }
 
     fn forward(&self, indexes: &Tensor) -> Result<Tensor> {
-        Tensor::embedding(indexes, &self.embeddings)
+        let values = Tensor::embedding(indexes, &self.embeddings)?;
+        Ok(values)
     }
 }
 
@@ -281,11 +284,15 @@ impl BertEmbeddings {
     }
 
     fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
+        let seq_len = input_ids.shape().r1()?;
         let input_embeddings = self.word_embeddings.forward(input_ids)?;
         let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?;
         let mut embeddings = (input_embeddings + token_type_embeddings)?;
         if let Some(position_embeddings) = &self.position_embeddings {
-            embeddings = (&embeddings + position_embeddings.forward(&embeddings))?
+            // TODO: Proper absolute positions?
+            let position_ids = (0..seq_len as u32).collect::<Vec<_>>();
+            let position_ids = Tensor::new(&position_ids[..], &input_ids.device())?;
+            embeddings = (&embeddings + position_embeddings.forward(&position_ids)?)?
         }
         let embeddings = self.layer_norm.forward(&embeddings)?;
         let embeddings = self.dropout.forward(&embeddings)?;
@@ -326,7 +333,8 @@ impl BertSelfAttention {
         new_x_shape.pop();
         new_x_shape.push(self.num_attention_heads);
         new_x_shape.push(self.attention_head_size);
-        xs.reshape(new_x_shape.as_slice())?.transpose(1, 2)
+        let xs = xs.reshape(new_x_shape.as_slice())?.transpose(1, 2)?;
+        Ok(xs)
     }
 
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
@@ -425,7 +433,8 @@ impl BertIntermediate {
 
     fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
         let hidden_states = self.dense.forward(hidden_states)?;
-        self.intermediate_act.forward(&hidden_states)
+        let ys = self.intermediate_act.forward(&hidden_states)?;
+        Ok(ys)
     }
 }
 
@@ -534,8 +543,8 @@ impl BertModel {
         })
     }
 
-    fn forward(&self, input_ids: &Tensor, position_ids: &Tensor) -> Result<Tensor> {
-        let embedding_output = self.embeddings.forward(input_ids, position_ids)?;
+    fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
+        let embedding_output = self.embeddings.forward(input_ids, token_type_ids)?;
         let sequence_output = self.encoder.forward(&embedding_output)?;
         Ok(sequence_output)
     }
@@ -555,7 +564,7 @@ struct Args {
     weights: String,
 }
 
-fn main() -> anyhow::Result<()> {
+fn main() -> Result<()> {
     use tokenizers::Tokenizer;
 
     let args = Args::parse();
@@ -579,9 +588,9 @@ fn main() -> anyhow::Result<()> {
         .get_ids()
         .to_vec();
     let token_ids = Tensor::new(&tokens[..], &device)?;
-    let position_ids: Vec<_> = (0..tokens.len() as u32).collect();
-    let position_ids = Tensor::new(&position_ids[..], &device)?.unsqueeze(0)?;
-    let ys = model.forward(&token_ids, &position_ids)?;
+    println!("{token_ids}");
+    let token_type_ids = token_ids.zeros_like()?;
+    let ys = model.forward(&token_ids, &token_type_ids)?;
     println!("{ys}");
     Ok(())
 }