Add new SentenceTransformer model.

.gitattributes

@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+0_Asym/140442697524080_Transformer/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+0_Asym/140438116837408_Transformer/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+1_Dense/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

0_Asym/140438116837408_Transformer/config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "/netdisk/xvg/smiles_3",
+  "architectures": [
+    "BertModel"
+  ],
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+  "layer_norm_eps": 1e-12,
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+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30000
+}

0_Asym/140438116837408_Transformer/pytorch_model.bin

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0_Asym/140438116837408_Transformer/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

0_Asym/140438116837408_Transformer/special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

0_Asym/140438116837408_Transformer/tokenizer.json

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0_Asym/140438116837408_Transformer/tokenizer_config.json

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0_Asym/140438116837408_Transformer/vocab.txt

@@ -0,0 +1,201 @@
+[PAD]
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0_Asym/140442693121648_Pooling/config.json

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+}

0_Asym/140442693122032_Dense/config.json

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+{"in_features": 1024, "out_features": 1024, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

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0_Asym/140442693122080_Pooling/config.json

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+}

0_Asym/140442694969664_Dense/config.json

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0_Asym/140442697524080_Transformer/config.json

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+{
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0_Asym/140442697524080_Transformer/sentence_bert_config.json

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+}

0_Asym/140442697524080_Transformer/special_tokens_map.json

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0_Asym/140442697524080_Transformer/tokenizer.json

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0_Asym/140442697524080_Transformer/tokenizer_config.json

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0_Asym/config.json

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+{
+  "types": {
+    "140442697524080_Transformer": "sentence_transformers.models.Transformer",
+    "140442693122080_Pooling": "sentence_transformers.models.Pooling",
+    "140442693122032_Dense": "sentence_transformers.models.Dense",
+    "140438116837408_Transformer": "sentence_transformers.models.Transformer",
+    "140442693121648_Pooling": "sentence_transformers.models.Pooling",
+    "140442694969664_Dense": "sentence_transformers.models.Dense"
+  },
+  "structure": {
+    "protein": [
+      "140442697524080_Transformer",
+      "140442693122080_Pooling",
+      "140442693122032_Dense"
+    ],
+    "ligand": [
+      "140438116837408_Transformer",
+      "140442693121648_Pooling",
+      "140442694969664_Dense"
+    ]
+  },
+  "parameters": {
+    "allow_empty_key": true
+  }
+}

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README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+---
+
+# jglaser/protein-ligand-mlp-3
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('jglaser/protein-ligand-mlp-3')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jglaser/protein-ligand-mlp-3)
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Asym(
+    (protein-0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel 
+    (protein-1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+    (protein-2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+    (ligand-0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+    (ligand-1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+    (ligand-2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
+  )
+  (1): Dense({'in_features': 1792, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
+  (2): Dense({'in_features': 1000, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
+  (3): Dense({'in_features': 1000, 'out_features': 1000, 'bias': True, 'activation_function': 'torch.nn.modules.activation.GELU'})
+  (4): Dense({'in_features': 1000, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
+  (5): Dense({'in_features': 1, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.17.0.dev0",
+    "pytorch": "1.10.2"
+  }
+}

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "0_Asym",
+    "type": "sentence_transformers.models.Asym"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
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+    "name": "4",
+    "path": "4_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 5,
+    "name": "5",
+    "path": "5_Dense",
+    "type": "sentence_transformers.models.Dense"
+  }
+]