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README.md

@@ -1,3 +1,121 @@
 ---
 license: apache-2.0
 ---
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss, KLDivLoss
+from transformers.modeling_outputs import TokenClassifierOutput
+from transformers import BertModel, BertPreTrainedModel
+
+class BertForHighlightPrediction(BertPreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config, **model_kwargs):
+        super().__init__(config)
+        # self.model_args = model_kargs["model_args"]
+        self.num_labels = config.num_labels
+        self.bert = BertModel(config, add_pooling_layer=False)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.tokens_clf = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.tau = model_kwargs.pop('tau', 1)
+        self.gamma = model_kwargs.pop('gamma', 1)
+        self.soft_labeling = model_kwargs.pop('soft_labeling', False)
+
+        self.init_weights()
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self,
+                input_ids=None,
+                probs=None, # soft-labeling
+                attention_mask=None,
+                token_type_ids=None,
+                position_ids=None,
+                head_mask=None,
+                inputs_embeds=None,
+                labels=None,
+                output_attentions=None,
+                output_hidden_states=None,
+                return_dict=None,):
+
+        outputs = self.bert(
+            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,
+        )
+
+        tokens_output = outputs[0]
+        highlight_logits = self.tokens_clf(self.dropout(tokens_output))
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            active_loss = attention_mask.view(-1) == 1
+            active_logits = highlight_logits.view(-1, self.num_labels)
+            active_labels = torch.where(
+                    active_loss,
+                    labels.view(-1),
+                    torch.tensor(loss_fct.ignore_index).type_as(labels)
+            )
+            loss_ce = loss_fct(active_logits, active_labels)
+
+            loss_kl = 0
+            if self.soft_labeling:
+                loss_fct = KLDivLoss(reduction='sum')
+                active_mask = (attention_mask * token_type_ids).view(-1, 1) # BL 1
+                n_active = (active_mask == 1).sum()
+                active_mask = active_mask.repeat(1, 2) # BL 2
+                input_logp = F.log_softmax(active_logits / self.tau, -1) # BL 2
+                target_p = torch.cat(( (1-probs).view(-1, 1), probs.view(-1, 1)), -1) # BL 2
+
+                loss_kl = loss_fct(input_logp, target_p * active_mask) / n_active
+
+            loss = self.gamma * loss_ce + (1-self.gamma) * loss_kl
+
+            # print("Loss:\n")
+            # print(loss)
+            # print(loss_kl)
+            # print(loss_ce)
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=highlight_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+    @torch.no_grad()
+    def inference(self, outputs):
+
+        with torch.no_grad():
+            outputs = self.forward(**batch_inputs)
+            probabilities = self.softmax(self.tokens_clf(outputs.hidden_states[-1]))
+            predictions = torch.argmax(probabilities, dim=-1)
+
+            # active filtering
+            active_tokens = batch_inputs['attention_mask'] == 1
+            active_predictions = torch.where(
+                active_tokens,
+                predictions,
+                torch.tensor(-1).type_as(predictions)
+            )
+
+            outputs = {
+                    "probabilities": probabilities[:, :, 1].detach(), # shape: (batch, length)
+                    "active_predictions": predictions.detach(),
+                    "active_tokens": active_tokens,
+            } 
+
+            return outputs
+```