Upload model
Browse files- config.json +0 -1
- configuration_cxrmate_ed.py +1 -4
- modelling_cxrmate_ed.py +3 -3
config.json
CHANGED
@@ -7,7 +7,6 @@
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"AutoConfig": "configuration_cxrmate_ed.CXRMateEDConfig",
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"AutoModelForCausalLM": "modelling_cxrmate_ed.CXRMateEDModel"
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},
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"hidden_size": 768,
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"history": 0,
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"include_time_delta": true,
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"index_value_encoder_intermediate_size": 2048,
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"AutoConfig": "configuration_cxrmate_ed.CXRMateEDConfig",
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"AutoModelForCausalLM": "modelling_cxrmate_ed.CXRMateEDModel"
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},
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"history": 0,
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"include_time_delta": true,
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"index_value_encoder_intermediate_size": 2048,
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configuration_cxrmate_ed.py
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@@ -62,10 +62,7 @@ class CXRMateEDConfig(transformers.PretrainedConfig):
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self.history = history
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self.tables_filter = tables_filter
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self.prompt_report_sections_filter = prompt_report_sections_filter
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self.pad_token_id = pad_token_id
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-
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self.hidden_size = self.text_config.hidden_size if self.text_config is not None else None
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# self.ignore_index = ignore_index
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# self.image_token_index = image_token_index
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self.history = history
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self.tables_filter = tables_filter
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self.prompt_report_sections_filter = prompt_report_sections_filter
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self.pad_token_id = pad_token_id
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# self.ignore_index = ignore_index
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# self.image_token_index = image_token_index
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modelling_cxrmate_ed.py
CHANGED
@@ -162,7 +162,7 @@ class CXRMateEDModel(transformers.LlavaForConditionalGeneration):
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FNNEncoder(
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num_features=self.luts[k]['total'],
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intermediate_size=self.config.index_value_encoder_intermediate_size,
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decoder_hidden_size=self.config.hidden_size,
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),
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)
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@@ -170,10 +170,10 @@ class CXRMateEDModel(transformers.LlavaForConditionalGeneration):
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self.time_delta_encoder = FNNEncoder(
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num_features=1,
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intermediate_size=self.config.index_value_encoder_intermediate_size,
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decoder_hidden_size=self.config.hidden_size,
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)
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self.token_type_embeddings = torch.nn.Embedding(max(self.token_type_to_token_type_id.values()) + 1, self.config.hidden_size)
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self.time_delta_map = lambda x: 1 / math.sqrt(x + 1)
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self.zero_time_delta_value = self.time_delta_map(0)
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FNNEncoder(
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num_features=self.luts[k]['total'],
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intermediate_size=self.config.index_value_encoder_intermediate_size,
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decoder_hidden_size=self.config.text_config.hidden_size,
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),
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)
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self.time_delta_encoder = FNNEncoder(
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num_features=1,
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intermediate_size=self.config.index_value_encoder_intermediate_size,
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decoder_hidden_size=self.config.text_config.hidden_size,
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)
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self.token_type_embeddings = torch.nn.Embedding(max(self.token_type_to_token_type_id.values()) + 1, self.config.text_config.hidden_size)
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self.time_delta_map = lambda x: 1 / math.sqrt(x + 1)
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self.zero_time_delta_value = self.time_delta_map(0)
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