add model

modeling_word2vec.py

@@ -1,4 +1,4 @@
-from transformers import PreTrainedModel
+from transformers import PreTrainedModel, modeling_outputs
 from torch import nn
 import torch
 from .configuration_word2vec import PretrainedWord2VecHFConfig
@@ -14,5 +14,7 @@ class PretrainedWord2VecHFModel(PreTrainedModel):
         self.embeddings = nn.Embedding.from_pretrained(torch.tensor(embeddings))
     
     def forward(self, input_ids, **kwargs):
-        x = self.embeddings(torch.tensor(input_ids))
-        return x
+        if type(input_ids) != torch.tensor:  # e.g., list or np.array
+            input_ids = torch.tensor(input_ids)
+        x = self.embeddings(input_ids)
+        return modeling_outputs.BaseModelOutput(last_hidden_state=x)