Upload NoRefER

config.json

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+{
+  "_name_or_path": "asr-qe",
+  "architectures": [
+    "NoRefER"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModel": "model.NoRefER"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.py

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+__author__="thiagocastroferreira"
+
+import torch.nn as nn
+from transformers import XLMRobertaModel
+from transformers.models.xlm_roberta.modeling_xlm_roberta import XLMRobertaPreTrainedModel
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+class Smish(nn.Module):
+    def __init__(self):
+        super().__init__()
+    def forward(self, x):
+        return x * (x.sigmoid() + 1).log().tanh()
+
+class NoRefER(XLMRobertaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        hidden_size = 32
+        self.config = config
+        self.roberta = XLMRobertaModel(config)
+        self.dense = nn.Sequential(
+            nn.Dropout(config.hidden_dropout_prob), 
+            nn.Linear(config.hidden_size, hidden_size, bias = False),
+            nn.Dropout(config.hidden_dropout_prob), Smish(), 
+            nn.Linear(hidden_size, 1, bias = False)
+        )
+
+        self.post_init()
+
+    def forward(self, positive_input_ids, positive_attention_mask, negative_input_ids, negative_attention_mask, labels, weight=None):
+        # positive processing
+        positive_inputs = {
+            "input_ids": positive_input_ids #, "attention_mask": positive_attention_mask
+        }
+        positive = self.dense(self.roberta(**positive_inputs).pooler_output).squeeze(-1)
+
+        # negative processing
+        negative_inputs = {
+            "input_ids": negative_input_ids #, "attention_mask": negative_attention_mask
+        }
+        negative = self.dense(self.roberta(**negative_inputs).pooler_output).squeeze(-1)
+
+        if weight is None:
+            bce = nn.BCEWithLogitsLoss()
+        else:
+            bs = len(positive)
+            weights = (weight.float() * bs) / weight.sum()
+            bce = nn.BCEWithLogitsLoss(weight = weights)
+        loss = bce(positive - negative, labels.float())
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=positive.sigmoid()-negative.sigmoid(),
+        )
+    
+    def score(
+        self,
+        input_ids,
+        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,
+    ):
+        h = self.roberta(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,).pooler_output
+
+        return self.dense(h).sigmoid().squeeze(-1)

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:615a33102224ff0ce4a25a462a1b28a2f59ea48ef1288d27078e6e20d0947d07
+size 470682677