diff --git a/spacy/_ml.py b/spacy/_ml.py
new file mode 100644
index 000000000..4c1a190b4
--- /dev/null
+++ b/spacy/_ml.py
@@ -0,0 +1,96 @@
+from thinc.api import layerize, chain, clone, concatenate, add
+from thinc.neural import Model, Maxout, Softmax
+from thinc.neural._classes.static_vectors import StaticVectors
+from thinc.neural._classes.hash_embed import HashEmbed
+from thinc.neural._classes.convolution import ExtractWindow
+
+from .attrs import ID, PREFIX, SUFFIX, SHAPE, TAG, DEP
+
+
+@layerize
+def get_contexts(states, drop=0.):
+    ops = Model.ops
+    context = ops.allocate((len(states), 7), dtype='uint64')
+    for i, state in enumerate(states):
+        context[i, 0] = state.B(0)
+        context[i, 1] = state.S(0)
+        context[i, 2] = state.S(1)
+        context[i, 3] = state.L(state.S(0), 1)
+        context[i, 4] = state.L(state.S(0), 2)
+        context[i, 5] = state.R(state.S(0), 1)
+        context[i, 6] = state.R(state.S(0), 2)
+    return (context, states), None
+
+def get_col(idx):
+    def forward(X, drop=0.):
+        return Model.ops.xp.ascontiguousarray(X[:, idx]), None
+    return layerize(forward)
+
+
+def extract_features(attrs):
+    ops = Model.ops
+    def forward(contexts_states, drop=0.):
+        contexts, states = contexts_states
+        output = ops.allocate((len(states), contexts.shape[1], len(attrs)),
+                              dtype='uint64')
+        for i, state in enumerate(states):
+            for j, tok_i in enumerate(contexts[i]):
+                token = state.get_token(tok_i)
+                for k, attr in enumerate(attrs):
+                    output[i, j, k] = getattr(token, attr)
+        return output, None
+    return layerize(forward)
+
+
+def build_tok2vec(lang, width, depth, embed_size):
+    cols = [ID, PREFIX, SUFFIX, SHAPE]
+    
+    with Model.define_operators({'>>': chain, '|': concatenate, '**': clone}):
+        static = get_col(cols.index(ID))     >> StaticVectors('en', width)
+        prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size)
+        suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size)
+        shape = get_col(cols.index(SHAPE))   >> HashEmbed(width, embed_size)
+        tok2vec = (
+            extract_features(cols)
+            >> (static | prefix | suffix | shape)
+            >> (ExtractWindow(nW=1) >> Maxout(width)) ** depth
+        )
+    return tok2vec
+
+
+def build_parse2vec(width, embed_size):
+    cols = [TAG, DEP]
+    with Model.define_operators({'>>': chain, '|': concatenate}):
+        tag_vector = get_col(cols.index(TAG)) >> HashEmbed(width, 1000)
+        dep_vector = get_col(cols.index(DEP)) >> HashEmbed(width, 1000)
+        model = (
+            extract_features([TAG, DEP])
+            >> (tag_vector | dep_vector)
+        )
+    return model
+ 
+
+def build_model(state2context, tok2vec, parse2vec, width, depth, nr_class):
+    with Model.define_operators({'>>': chain, '**': clone, '|': concatenate}):
+        model = (
+            state2context
+            >> (tok2vec | parse2vec)
+            >> Maxout(width) ** depth
+            >> Softmax(nr_class)
+        )
+    return model
+
+
+def test_build_model(width=100, depth=2, nr_class=10):
+    model = build_model(
+                get_contexts,
+                build_tok2vec('en', width=100, depth=2, embed_size=1000),
+                build_parse2vec(width=100, embed_size=1000),
+                width,
+                depth,
+                nr_class)
+    assert model is not None
+
+
+if __name__ == '__main__':
+    test_build_model()