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Model: Shobhank-iiitdwd/BERT-BU-nli-sts-sent
Source: Original Platform

.gitattributes

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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "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
+language: en
+tags:
+- sentence-similarity
+- transformers
+- en
+- bert
+- sentence-transformers
+- feature-extraction
+datasets:
+- nli
+- stsb
+---
+
+It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+## Details
+
+This model is based on the English bert-base-uncased pre-trained model [1, 2]. 
+
+
+Model was trained  on a natural language inference task (NLI). This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication).
+
+It was then trained on a text semantic similarity task (on STS data) [3]. This task consists in training the model to estimate the similarity between two sentences.
+
+This fine-tuning process allows our model to have a semantic representation of words that is much better than the one proposed by the base model.
+
+## 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 = ["How is air traffic controlled?", "How do you become an air traffic controller?"]
+
+model = SentenceTransformer('Shobhank-iiitdwd/BERT-BU-nli-sts-sent')
+embeddings = model.encode(sentences)
+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
+sentences = ["Learn to code in python", "Become an expert in accounting"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('Shobhank-iiitdwd/BERT-BU-nli-sts-sent')
+model = AutoModel.from_pretrained('Shobhank-iiitdwd/BERT-BU-nli-sts-sent')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, 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'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+STS (en) score: 84.61%
+
+
+## Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## References
+
+[1] https://huggingface.co/bert-base-uncased <br>
+[2] https://arxiv.org/abs/1810.04805 <br>
+[3] https://huggingface.co/datasets/stsb_multi_mt <br>

config.json

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+{
+  "_name_or_path": "inokufu/bert-base-uncased-xnli-sts-finetuned-education",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.1.0",
+    "transformers": "4.18.0",
+    "pytorch": "1.10.0+cu111"
+  }
+}

eval/similarity_evaluation_sts-dev_results.csv

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index.gitattributes

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
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+    "type": "sentence_transformers.models.Pooling"
+  }
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sentence_bert_config.json

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+  "max_seq_length": 512,
+  "do_lower_case": true
+}

similarity_evaluation_sts-test_results.csv

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special_tokens_map.json

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tokenizer_config.json

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