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README.md

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+# Sentence Embeddings Models trained on Duplicate Questions
+This model is from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers)-repository. It was trained on the [Quora Duplicate Questions dataset](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs). Further details on SBERT can be found in the paper:  [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
+
+For more details, see: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)
+
+## Usage (HuggingFace Models Repository)
+
+You can use the model directly from the model repository to compute sentence 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()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+
+
+
+#Sentences we want sentence embeddings for
+sentences = ['This framework generates embeddings for each input sentence',
+             'Sentences are passed as a list of string.',
+             'The quick brown fox jumps over the lazy dog.']
+
+#Load AutoModel from huggingface model repository
+tokenizer = AutoTokenizer.from_pretrained("model_name")
+model = AutoModel.from_pretrained("model_name")
+
+#Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
+
+#Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+#Perform pooling. In this case, mean pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+```
+
+## Usage (Sentence-Transformers)
+Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('model_name')
+sentences = ['This framework generates embeddings for each input sentence',
+    'Sentences are passed as a list of string.', 
+    'The quick brown fox jumps over the lazy dog.']
+sentence_embeddings = model.encode(sentences)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Citing & Authors
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+``` 
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "http://arxiv.org/abs/1908.10084",
+}
+```

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68d315c1638882d9c6cd8901ed497a24de830506773274f60f477620002e113d
+size 265473819

sentence_bert_config.json

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+{
+  "max_seq_length": 128
+}

special_tokens_map.json

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

tokenizer_config.json

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+{"do_lower_case": true, "model_max_length": 512, "special_tokens_map_file": "output/training_nli_distilbert-base-uncased-2020-07-22_10-20-15/0_Transformer/special_tokens_map.json", "full_tokenizer_file": null}