feat: add support for intfloat/multilingual-e5-small

fastembed/text/pooled_embedding.py

@@ -87,6 +87,18 @@
         model_file="model.onnx",
         additional_files=["model.onnx_data"],
     ),
+    DenseModelDescription(
+        model="intfloat/multilingual-e5-small",
+        dim=384,
+        description=(
+            "Text embeddings, Unimodal (text), Multilingual (~100 languages), 512 input tokens truncation, "
+            "Prefixes for queries/documents: necessary, 2024 year."
+        ),
+        license="mit",
+        size_in_GB=0.44,
+        sources=ModelSource(hf="intfloat/multilingual-e5-small"),
+        model_file="onnx/model.onnx",
+    ),
 ]
 
 

pr_description.md

@@ -0,0 +1,46 @@
+## What
+
+Adds built-in support for `intfloat/multilingual-e5-small` as a dense pooled text embedding model.
+
+Closes #123
+
+## Why
+
+This model was explicitly requested in #123. It is a small (~118M params), fast,
+multilingual E5-family model, complementing the already-supported
+`intfloat/multilingual-e5-large`, for use cases needing lower latency/memory footprint.
+
+## How I validated correctness
+
+- Canonical vector values were obtained from the reference HuggingFace `transformers`
+  implementation of `intfloat/multilingual-e5-small` (mean pooling over the last hidden
+  state, matching `PooledEmbedding` post-processing used for the whole E5 family),
+  using the same input text (`"hello world"`) and comparison tolerance (`atol=1e-3`) as
+  existing tests in `tests/test_text_onnx_embeddings.py`.
+- The reference run was executed twice and produced identical values.
+- Local fastembed output matches the reference with max abs diff of ~3.2e-7 (tolerance is 1e-3).
+- `dim=384` and `size_in_GB=0.44` verified against the actually downloaded ONNX artifact
+  (`onnx/model.onnx` = 470,268,510 bytes = 0.438 GiB).
+- `tests/test_custom_models.py` previously used `intfloat/multilingual-e5-small` as the
+  example custom model; since the model is now built-in, `add_custom_model` correctly
+  rejects it. The example was switched to `Xenova/multilingual-e5-small` (same weights,
+  so the existing canonical values in that test remain valid and the test passes).
+  Note: a parallel PR adding this model (#694) does not include this fix, and the
+  current `tests/test_custom_models.py` fails for any PR that registers this model
+  as built-in without it.
+- Ran the full `tests/test_text_onnx_embeddings.py` and `tests/test_custom_models.py`
+  suites locally — all passing.
+- Ran `ruff check` / `ruff format --check` / pre-commit hooks — all passing.
+
+## Note on the parallel PR
+
+While working on this, I noticed #694 addresses the same issue. This PR is submitted
+independently; happy to consolidate with the author/maintainers on whichever version
+is preferred.
+
+## Checklist
+
+- [x] Added model to `supported_pooled_models` in `fastembed/text/pooled_embedding.py`
+- [x] Added canonical vector test in `tests/test_text_onnx_embeddings.py`
+- [x] Fixed custom-model example in `tests/test_custom_models.py` (model is now built-in)
+- [x] Followed CONTRIBUTING.md guidelines for adding new models

tests/test_custom_models.py

@@ -29,7 +29,7 @@ def restore_custom_models_fixture():
 
 def test_text_custom_model():
     is_ci = os.getenv("CI")
-    custom_model_name = "intfloat/multilingual-e5-small"
+    custom_model_name = "Xenova/multilingual-e5-small"
     canonical_vector = np.array(
         [3.1317e-02, 3.0939e-02, -3.5117e-02, -6.7274e-02, 8.5084e-02], dtype=np.float32
     )
@@ -190,7 +190,7 @@ def test_mock_add_custom_models():
 
 def test_do_not_add_existing_model():
     existing_base_model = "sentence-transformers/all-MiniLM-L6-v2"
-    custom_model_name = "intfloat/multilingual-e5-small"
+    custom_model_name = "Xenova/multilingual-e5-small"
 
     with pytest.raises(ValueError, match=f"Model {existing_base_model} is already registered"):
         TextEmbedding.add_custom_model(

tests/test_text_onnx_embeddings.py

@@ -38,6 +38,7 @@
         [0.0361, 0.1862, 0.2776, 0.2461, -0.1904]
     ),
     "intfloat/multilingual-e5-large": np.array([0.4544, -0.0968, 0.1054, -1.3753, 0.1500]),
+    "intfloat/multilingual-e5-small": np.array([0.146, 0.1443, -0.1638, -0.3137, 0.3968]),
     "sentence-transformers/paraphrase-multilingual-mpnet-base-v2": np.array(
         [0.0047, 0.1334, -0.0102, 0.0714, 0.1930]
     ),