add model code

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "RobertaNFQAClassification"
   ],
+  "auto_map": {
+    "AutoConfig": "RobertaConfig",
+    "AutoModelForImageClassification": "nfqa_model.RobertaNFQAClassification"
+  },
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
   "eos_token_id": 2,
@@ -40,6 +44,7 @@
   "num_hidden_layers": 12,
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
   "transformers_version": "4.2.2",
   "type_vocab_size": 1,
   "use_cache": true,

nfqa_model.py

@@ -0,0 +1,105 @@
+from typing import Sequence, Optional, Union, Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional, CrossEntropyLoss
+from transformers import RobertaConfig
+from transformers.modeling_outputs import SequenceClassifierOutput
+from transformers.models.roberta.modeling_roberta import RobertaModel, RobertaPreTrainedModel, RobertaPooler
+
+
+class MishActivation(nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x * torch.tanh(torch.nn.functional.softplus(x))
+
+
+class NFQAClassificationHead(nn.Module):
+    def __init__(
+            self, input_dim: int, num_labels: int, hidden_dims: Sequence[int] = (768, 512), dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+
+        self.linear_layers = nn.Sequential(
+            *(nn.Linear(input_dim, dim) for dim in hidden_dims)
+        )
+        self.classification_layer = torch.nn.Linear(hidden_dims[-1], num_labels)
+        self.activations = [MishActivation()] * len(hidden_dims)
+        self.dropouts = [torch.nn.Dropout(p=dropout)] * len(hidden_dims)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        output = inputs
+        for layer, activation, dropout in zip(
+                self.linear_layers, self.activations, self.dropouts
+        ):
+            output = dropout(activation(layer(output)))
+        return self.classification_layer(output)
+
+
+class RobertaNFQAClassification(RobertaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+    _DROPOUT = 0.0
+
+    def __init__(self, config: RobertaConfig):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.embedder = RobertaModel(config, add_pooling_layer=True)
+        self.pooler = RobertaPooler(config)
+        self.feedforward = NFQAClassificationHead(config.hidden_size, config.num_labels)
+        self.dropout = torch.nn.Dropout(self._DROPOUT)
+
+        self.init_weights()
+
+
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            token_type_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.embedder(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+
+        logits = self.feedforward(self.dropout(self.pooler(sequence_output)))
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )