Mirror kuelumbus/polyBERT (original removed from Hub)

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 600,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+
+widget:
+- source_sentence: "[*]CC[*]"
+  sentences:
+    - "[*]COC[*]"
+    - "[*]CC(C)C[*]"
+---
+
+# kuelumbus/polyBERT
+
+This is polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics. polyBERT maps PSMILES strings to 600 dimensional dense fingerprints. The fingerprints numerically represent polymer chemical structures. Please see the license agreement in the LICENSE file.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
+
+polyBERT = SentenceTransformer('kuelumbus/polyBERT')
+embeddings = polyBERT.encode(psmiles_strings)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('kuelumbus/polyBERT')
+polyBERT = AutoModel.from_pretrained('kuelumbus/polyBERT')
+
+# Tokenize sentences
+encoded_input = tokenizer(psmiles_strings, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = polyBERT(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+fingerprints = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Fingerprints:")
+print(fingerprints)
+```
+
+
+
+## Evaluation Results
+
+See https://github.com/Ramprasad-Group/polyBERT and paper on arXiv.
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
+  (1): Pooling({'word_embedding_dimension': 600, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+Kuenneth, C., Ramprasad, R. polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nat Commun 14, 4099 (2023). https://doi.org/10.1038/s41467-023-39868-6

added_tokens.json

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+{
+  "[CLS]": 265,
+  "[MASK]": 268,
+  "[PAD]": 267,
+  "[SEP]": 266,
+  "[UNK]": 269
+}

config.json

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+{
+  "_name_or_path": "polyBERT_sentence_transformers/",
+  "architectures": [
+    "DebertaV2Model"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 600,
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 3,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 600,
+  "pos_att_type": null,
+  "position_biased_input": true,
+  "relative_attention": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.0",
+  "type_vocab_size": 0,
+  "vocab_size": 269
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.18.0",
+    "pytorch": "1.10.2+cu102"
+  }
+}

model.safetensors

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+size 100800352

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

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+size 100839543

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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

spm.model

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+size 242316

tokenizer_config.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "name_or_path": "polyBERT_sentence_transformers/",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": null,
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]"
+}