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1_Pooling/config.json

@@ -1,5 +1,5 @@
 {
-  "word_embedding_dimension": 128,
+  "word_embedding_dimension": 312,
   "pooling_mode_cls_token": true,
   "pooling_mode_mean_tokens": false,
   "pooling_mode_max_tokens": false,

README.md

@@ -10,7 +10,7 @@ library_name: sentence-transformers
 
 # SentenceTransformer based on cointegrated/rubert-tiny
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny). It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny). It maps sentences & paragraphs to a 312-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
@@ -18,7 +18,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [c
 - **Model Type:** Sentence Transformer
 - **Base model:** [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) <!-- at revision 5441c5ea8026d4f6d7505ec004845409f1259fb1 -->
 - **Maximum Sequence Length:** 256 tokens
-- **Output Dimensionality:** 128 tokens
+- **Output Dimensionality:** 312 tokens
 - **Similarity Function:** Cosine Similarity
 <!-- - **Training Dataset:** Unknown -->
 <!-- - **Language:** Unknown -->
@@ -35,7 +35,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [c
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 128, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
@@ -63,7 +63,7 @@ sentences = [
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
-# [3, 128]
+# [3, 312]
 
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)