diff --git a/fastai_contrib/models.py b/fastai_contrib/models.py
index 92ac7e4..61a84c1 100644
--- a/fastai_contrib/models.py
+++ b/fastai_contrib/models.py
@@ -70,6 +70,120 @@ class MultiBatchBiLMModel(BiLMModel):
                 outputs.append(o)
         return self.concat(raw_outputs), self.concat(outputs)
 
+class BiAttentionPoolingClassifier(nn.Module):
+    r" [WIP] BiLM Pooling with self attention"
+
+    def __init__(self, layers:Collection[int], drops:Collection[float]):
+        super().__init__()
+        mod_layers = []
+        activs = [nn.ReLU(inplace=True)] * (len(layers) - 2) + [None]
+        for n_in,n_out,p,actn in zip(layers[:-1],layers[1:], drops, activs):
+            mod_layers += bn_drop_lin(n_in, n_out, p=p, actn=actn)
+        self.self_attn = MultiHeadAttention(n_head=8, d_model=1, d_k=64, d_v=64, dropout=0.1)
+        self.layers = nn.Sequential(*mod_layers)
+
+    def pool(self, x:Tensor, bs:int, is_max:bool):
+        "Pool the tensor along the seq_len dimension."
+        f = F.adaptive_max_pool1d if is_max else F.adaptive_avg_pool1d
+        return f(x.permute(1,2,0), (1,)).view(bs,-1)
+    
+    def forward(self, input:Tuple[Tensor,Tensor])->Tuple[Tensor,Tensor,Tensor]:
+        raw_outputs, outputs = input
+        output = outputs[-1]
+        assert len(output.size()) == 4, 'Expected input dimension 4'
+        sl, bs, em_sz, passes = output.size()
+
+        f_avgpool = self.pool(output[...,0], bs, False)
+        f_mxpool = self.pool(output[...,0], bs, True)
+        b_avgpool = self.pool(output[..., 1], bs, False)
+        b_mxpool = self.pool(output[..., 1], bs, True)
+        x = torch.cat([output[-1][..., 0], f_mxpool, f_avgpool,
+                        output[-1][..., 1], b_mxpool, b_avgpool,], 1)
+        x = x.unsqueeze(-1)
+        x, _ = self.self_attn(x, x, x)
+        
+        x = self.layers(x)
+        return x, raw_outputs, outputs
+
+class ScaledDotProductAttention(nn.Module):
+    r""" 
+    Scaled Dot-Product Attention
+    based on: https://github.com/jadore801120/attention-is-all-you-need-pytorch
+    """
+
+    def __init__(self, temperature, attn_dropout=0.1):
+        super().__init__()
+        self.temperature = temperature
+        self.dropout = nn.Dropout(attn_dropout)
+        self.softmax = nn.Softmax(dim=2)
+
+    def forward(self, q, k, v):
+
+        attn = torch.bmm(q, k.transpose(1, 2))
+        attn = attn / self.temperature
+
+        attn = self.softmax(attn)
+        attn = self.dropout(attn)
+        output = torch.bmm(attn, v)
+
+        return output, attn
+
+class MultiHeadAttention(nn.Module):
+    r""" 
+    Multi-Head Attention module
+    based on: https://github.com/jadore801120/attention-is-all-you-need-pytorch
+    """
+
+    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
+        super().__init__()
+
+        self.n_head = n_head
+        self.d_k = d_k
+        self.d_v = d_v
+
+        self.w_qs = nn.Linear(d_model, n_head * d_k)
+        self.w_ks = nn.Linear(d_model, n_head * d_k)
+        self.w_vs = nn.Linear(d_model, n_head * d_v)
+        nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
+        nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
+        nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))
+
+        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
+        self.layer_norm = nn.LayerNorm(d_model)
+
+        self.fc = nn.Linear(n_head * d_v, d_model)
+        nn.init.xavier_normal_(self.fc.weight)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, q, k, v):
+
+        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
+
+        
+        sz_b, len_q, _ = q.size()
+        sz_b, len_k, _ = k.size()
+        sz_b, len_v, _ = v.size()
+        residual = q
+
+        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
+        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
+        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
+
+        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
+        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
+        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
+
+        x, attn = self.attention(q, k, v)
+
+        x = x.view(n_head, sz_b, len_q, d_v)
+        x = x.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv)
+
+        x = self.dropout(self.fc(x))
+        x = self.layer_norm(x + residual)
+
+        return x, attn    
+
 class BiPoolingLinearClassifier(PoolingLinearClassifier):
     "Create a linear classifier with pooling."
 
@@ -163,4 +277,4 @@ def get_birnn_classifier(bptt:int, max_seq:int, n_class:int, vocab_sz:int, emb_s
     model.reset()
     return model
 
-#endregion
\ No newline at end of file
+#endregion