Upload model

hf.py

@@ -0,0 +1,88 @@
+from typing import Tuple, Union
+
+import torch
+from transformers import PretrainedConfig
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
+from transformers.models.bert.modeling_bert import BertModel
+
+
+class GoldenRetrieverConfig(PretrainedConfig):
+    model_type = "bert"
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        position_embedding_type="absolute",
+        use_cache=True,
+        classifier_dropout=None,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.classifier_dropout = classifier_dropout
+
+
+class GoldenRetrieverModel(BertModel):
+    config_class = GoldenRetrieverConfig
+
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(config)
+        self.layer_norm_layer = torch.nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+
+    def forward(
+        self, **kwargs
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        attention_mask = kwargs.get("attention_mask", None)
+        model_outputs = super().forward(**kwargs)
+        if attention_mask is None:
+            pooler_output = model_outputs.pooler_output
+        else:
+            token_embeddings = model_outputs.last_hidden_state
+            input_mask_expanded = (
+                attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+            )
+            pooler_output = torch.sum(
+                token_embeddings * input_mask_expanded, 1
+            ) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+            pooler_output = self.layer_norm_layer(pooler_output)
+
+        if not kwargs.get("return_dict", True):
+            return (model_outputs[0], pooler_output) + model_outputs[2:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=model_outputs.last_hidden_state,
+            pooler_output=pooler_output,
+            past_key_values=model_outputs.past_key_values,
+            hidden_states=model_outputs.hidden_states,
+            attentions=model_outputs.attentions,
+            cross_attentions=model_outputs.cross_attentions,
+        )

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4bb2233f9b9c051ab4f9c1b7f8c8bbe83dde252420cc19e07be6dced53715471
+oid sha256:cedb009f460ea8ff29d0acaec36f92f7e89edff73e55d5d85c96b0ba41bed25a
 size 437999665