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Gla-AI4BioMed-Lab

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FusionGDA

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Running

• "transformers.models.esm.configuration_esm.EsmConfig",
• "torch.nn.modules.container.ModuleList",
• "utils.metric_learning_models.GDA_Metric_Learning",
• "transformers.models.esm.modeling_esm.EsmSelfOutput",
• "transformers.models.bert.modeling_bert.BertAttention",
• "transformers.models.bert.modeling_bert.BertSelfOutput",
• "transformers.models.esm.modeling_esm.EsmSelfAttention",
• "torch.nn.modules.linear.NonDynamicallyQuantizableLinear",
• "pytorch_metric_learning.reducers.mean_reducer.MeanReducer",
• "torch.nn.modules.activation.Sigmoid",
• "transformers.models.esm.modeling_esm.EsmEncoder",
• "transformers.models.bert.modeling_bert.BertOutput",
• "torch.nn.modules.activation.MultiheadAttention",
• "collections.OrderedDict",
• "transformers.models.esm.modeling_esm.EsmLayer",
• "torch.LongStorage",
• "transformers.models.esm.modeling_esm.EsmModel",
• "transformers.models.bert.modeling_bert.BertEmbeddings",
• "transformers.models.esm.modeling_esm.EsmOutput",
• "transformers.activations.GELUActivation",
• "torch.nn.modules.activation.Tanh",
• "__builtin__.set",
• "torch._C._nn.gelu",
• "torch.nn.modules.sparse.Embedding",
• "transformers.models.bert.modeling_bert.BertLayer",
• "pytorch_metric_learning.distances.cosine_similarity.CosineSimilarity",
• "torch.nn.modules.linear.Linear",
• "__builtin__.getattr",
• "torch._utils._rebuild_parameter",
• "utils.metric_learning_models.FusionModule",
• "torch.FloatStorage",
• "transformers.models.esm.modeling_esm.EsmPooler",
• "transformers.models.esm.modeling_esm.EsmContactPredictionHead",
• "transformers.models.esm.modeling_esm.EsmAttention",
• "torch.float32",
• "transformers.models.esm.modeling_esm.EsmEmbeddings",
• "transformers.models.bert.configuration_bert.BertConfig",
• "utils.metric_learning_models.CrossAttentionBlock",
• "torch.nn.modules.dropout.Dropout",
• "transformers.models.bert.modeling_bert.BertSelfAttention",
• "transformers.models.esm.modeling_esm.EsmIntermediate",
• "transformers.models.esm.modeling_esm.RotaryEmbedding",
• "torch.nn.modules.normalization.LayerNorm",
• "transformers.models.bert.modeling_bert.BertEncoder",
• "pytorch_metric_learning.losses.ntxent_loss.NTXentLoss",
• "torch._utils._rebuild_tensor_v2",
• "transformers.models.bert.modeling_bert.BertPooler",
• "transformers.models.bert.modeling_bert.BertIntermediate",
• "transformers.models.bert.modeling_bert.BertModel"

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