Initial

0_WordEmbeddings/pytorch_model.bin

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0_WordEmbeddings/rust_model.ot

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0_WordEmbeddings/wordembedding_config.json

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+{
+  "tokenizer_class": "sentence_transformers.models.tokenizer.WhitespaceTokenizer.WhitespaceTokenizer",
+  "update_embeddings": false,
+  "max_seq_length": 1000000
+}

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 300,
+  "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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 ---
 license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+pipeline_tag: sentence-similarity
 ---
+
+# average_word_embeddings_glove.6B.300d
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+
+
+## 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('sentence-transformers/average_word_embeddings_glove.6B.300d')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/average_word_embeddings_glove.6B.300d)
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): WordEmbeddings(
+    (emb_layer): Embedding(400001, 300)
+  )
+  (1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+This model was trained by [sentence-transformers](https://www.sbert.net/). 
+        
+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):
+```bibtex 
+@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_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

modules.json

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