diff --git a/crates/milli/src/vector/embedder/hf.rs b/crates/milli/src/vector/embedder/hf.rs
index 3c51da5bb..d4d4a7bad 100644
--- a/crates/milli/src/vector/embedder/hf.rs
+++ b/crates/milli/src/vector/embedder/hf.rs
@@ -2,6 +2,7 @@ use candle_core::Tensor;
 use candle_nn::VarBuilder;
 use candle_transformers::models::bert::{BertModel, Config as BertConfig, DTYPE};
 use candle_transformers::models::modernbert::{Config as ModernConfig, ModernBert};
+use candle_transformers::models::xlm_roberta::{Config as XlmRobertaConfig, XLMRobertaModel};
 // FIXME: currently we'll be using the hub to retrieve model, in the future we might want to embed it into Meilisearch itself
 use hf_hub::api::sync::Api;
 use hf_hub::{Repo, RepoType};
@@ -89,6 +90,7 @@ impl Default for EmbedderOptions {
 enum ModelKind {
     Bert(BertModel),
     Modern(ModernBert),
+    XlmRoberta(XLMRobertaModel),
 }
 
 /// Perform embedding of documents and queries
@@ -304,7 +306,8 @@ impl Embedder {
         };
 
         let is_modern = has_arch("modernbert");
-        tracing::debug!(is_modern, model_type, "detected HF architecture");
+        let is_xlm_roberta = has_arch("xlm-roberta") || has_arch("xlm_roberta");
+        tracing::debug!(is_modern, is_xlm_roberta, model_type, "detected HF architecture");
 
         let mut tokenizer = Tokenizer::from_file(&tokenizer_filename)
             .map_err(|inner| NewEmbedderError::open_tokenizer(tokenizer_filename, inner))?;
@@ -340,6 +343,19 @@ impl Embedder {
                 )
             })?;
             ModelKind::Modern(ModernBert::load(vb, &config).map_err(NewEmbedderError::load_model)?)
+        } else if is_xlm_roberta {
+            let config: XlmRobertaConfig =
+                serde_json::from_str(&config_str).map_err(|inner| {
+                    NewEmbedderError::deserialize_config(
+                        options.model.clone(),
+                        config_str.clone(),
+                        config_filename.clone(),
+                        inner,
+                    )
+                })?;
+            ModelKind::XlmRoberta(
+                XLMRobertaModel::new(&config, vb).map_err(NewEmbedderError::load_model)?,
+            )
         } else {
             let config: BertConfig = serde_json::from_str(&config_str).map_err(|inner| {
                 NewEmbedderError::deserialize_config(
@@ -451,6 +467,19 @@ impl Embedder {
                 let mask = Tensor::stack(&[mask], 0).map_err(EmbedError::tensor_shape)?;
                 model.forward(&token_ids, &mask).map_err(EmbedError::model_forward)?
             }
+            ModelKind::XlmRoberta(model) => {
+                let mut mask_vec = tokens.get_attention_mask().to_vec();
+                if mask_vec.len() > self.max_len {
+                    mask_vec.truncate(self.max_len);
+                }
+                let mask = Tensor::new(mask_vec.as_slice(), &self.device)
+                    .map_err(EmbedError::tensor_shape)?;
+                let mask = Tensor::stack(&[mask], 0).map_err(EmbedError::tensor_shape)?;
+                let token_type_ids = token_ids.zeros_like().map_err(EmbedError::tensor_shape)?;
+                model
+                    .forward(&token_ids, &mask, &token_type_ids, None, None, None)
+                    .map_err(EmbedError::model_forward)?
+            }
         };
 
         let embedding = Self::pooling(embeddings, self.pooling)?;