Add jina_bert_test.exs

test/bumblebee/text/jina_bert_test.exs

@@ -0,0 +1,84 @@
+defmodule Bumblebee.Text.JinaBertTest do
+  use ExUnit.Case, async: true
+
+  import Bumblebee.TestHelpers
+
+  @moduletag model_test_tags()
+
+  @tag slow: true
+  test "jina-embeddings-v2-small-en" do
+    repo = {:hf, "jinaai/jina-embeddings-v2-small-en"}
+
+    {:ok, %{model: model, params: params, spec: _spec}} =
+      Bumblebee.load_model(repo,
+        params_filename: "model.safetensors",
+        spec_overrides: [architecture: :base]
+      )
+
+    inputs = %{
+      "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]),
+      "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
+    }
+
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.all_close(
+             outputs.hidden_state[[.., 1..3, 1..3]],
+             Nx.tensor([
+               [-0.1346, 0.1457, 0.5572],
+               [-0.1383, 0.1412, 0.5643],
+               [-0.1125, 0.1354, 0.5599]
+             ])
+           )
+  end
+
+  @tag :skip
+  test ":base" do
+    repo = {:hf, "doesnotexist/tiny-random-JinaBert"}
+
+    assert {:ok, %{model: model, params: params, spec: spec}} =
+             Bumblebee.load_model(repo)
+
+    assert %Bumblebee.Text.JinaBert{architecture: :base} = spec
+
+    inputs = %{
+      "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]),
+      "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
+    }
+
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.shape(outputs.hidden_state) == {1, 10, 32}
+
+    assert_all_close(
+      outputs.hidden_state[[.., 1..3, 1..3]],
+      Nx.tensor([
+        [[-0.2331, 1.7817, 1.1736], [-1.1001, 1.3922, -0.3391], [0.0408, 0.8677, -0.0779]]
+      ])
+    )
+  end
+
+  @tag :skip
+  test ":for_masked_language_modeling" do
+    repo = {:hf, "doesnotexist/tiny-random-JinaBert"}
+
+    assert {:ok, %{model: model, params: params, spec: spec}} =
+             Bumblebee.load_model(repo)
+
+    assert %Bumblebee.Text.Bert{architecture: :for_masked_language_modeling} = spec
+
+    inputs = %{
+      "input_ids" => Nx.tensor([[10, 20, 30, 40, 50, 60, 70, 80, 0, 0]]),
+      "attention_mask" => Nx.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
+    }
+
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.shape(outputs.logits) == {1, 10, 1124}
+
+    assert_all_close(
+      outputs.logits[[.., 1..3, 1..3]],
+      Nx.tensor([[[-0.0127, 0.0508, 0.0904], [0.1151, 0.1189, 0.0922], [0.0089, 0.1132, -0.2470]]])
+    )
+  end
+end