Add new SentenceTransformer model

1_Pooling/config.json

@@ -1,7 +1,7 @@
 {
     "word_embedding_dimension": 768,
-    "pooling_mode_cls_token": false,
-    "pooling_mode_mean_tokens": true,
+    "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,

README.md

@@ -14,7 +14,7 @@ tags:
 - generated_from_trainer
 - dataset_size:9233417
 - loss:ArcFaceInBatchLoss
-base_model: answerdotai/ModernBERT-base
+base_model: Alibaba-NLP/gte-modernbert-base
 widget:
 - source_sentence: Hayley Vaughan portrayed Ripa on the ABC daytime soap opera , ``
     All My Children `` , between 1990 and 2002 .
@@ -79,34 +79,34 @@ model-index:
       type: test
     metrics:
     - type: cosine_accuracy@1
-      value: 0.4134460403309574
+      value: 0.5861241448475948
       name: Cosine Accuracy@1
     - type: cosine_precision@1
-      value: 0.4134460403309574
+      value: 0.5861241448475948
       name: Cosine Precision@1
     - type: cosine_recall@1
-      value: 0.39978666009460445
+      value: 0.5679885764966713
       name: Cosine Recall@1
     - type: cosine_ndcg@10
-      value: 0.5951872317507402
+      value: 0.7729838064849864
       name: Cosine Ndcg@10
     - type: cosine_mrr@1
-      value: 0.4134460403309574
+      value: 0.5861241448475948
       name: Cosine Mrr@1
     - type: cosine_map@100
-      value: 0.5433812918329537
+      value: 0.7216697804426214
       name: Cosine Map@100
 ---
 
 # Redis fine-tuned BiEncoder model for semantic caching on LangCache
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
 - **Maximum Sequence Length:** 100 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
@@ -126,7 +126,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [a
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
-  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (1): Pooling({'word_embedding_dimension': 768, '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})
 )
 ```
 
@@ -159,9 +159,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[0.9961, 0.9922, 0.9922],
-#         [0.9922, 1.0000, 1.0000],
-#         [0.9922, 1.0000, 1.0000]], dtype=torch.bfloat16)
+# tensor([[1.0000, 0.9961, 0.9922],
+#         [0.9961, 1.0000, 0.9961],
+#         [0.9922, 0.9961, 0.9961]], dtype=torch.bfloat16)
 ```
 
 <!--
@@ -197,14 +197,14 @@ You can finetune this model on your own dataset.
 * Dataset: `test`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric             | Value      |
-|:-------------------|:-----------|
-| cosine_accuracy@1  | 0.4134     |
-| cosine_precision@1 | 0.4134     |
-| cosine_recall@1    | 0.3998     |
-| **cosine_ndcg@10** | **0.5952** |
-| cosine_mrr@1       | 0.4134     |
-| cosine_map@100     | 0.5434     |
+| Metric             | Value     |
+|:-------------------|:----------|
+| cosine_accuracy@1  | 0.5861    |
+| cosine_precision@1 | 0.5861    |
+| cosine_recall@1    | 0.568     |
+| **cosine_ndcg@10** | **0.773** |
+| cosine_mrr@1       | 0.5861    |
+| cosine_map@100     | 0.7217    |
 
 <!--
 ## Bias, Risks and Limitations
@@ -277,7 +277,7 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch | Step | test_cosine_ndcg@10 |
 |:-----:|:----:|:-------------------:|
-| -1    | -1   | 0.5952              |
+| -1    | -1   | 0.7730              |
 
 
 ### Framework Versions

config_sentence_transformers.json

@@ -1,5 +1,4 @@
 {
-  "model_type": "SentenceTransformer",
   "__version__": {
     "sentence_transformers": "5.1.0",
     "transformers": "4.56.0",
@@ -10,5 +9,6 @@
     "document": ""
   },
   "default_prompt_name": null,
-  "similarity_fn_name": "cosine"
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer"
 }

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:275fea20b9e3f51da926fce266f04e7d3defcc7a3143e947a76c09a5afaaa03c
+oid sha256:95d02211c4cca89113f9f3e93ed91f5176bf50170faa2cb835f7bfea15bb9dd2
 size 298041696

tokenizer_config.json

@@ -938,7 +938,7 @@
     "input_ids",
     "attention_mask"
   ],
-  "model_max_length": 8192,
+  "model_max_length": 1000000000000000019884624838656,
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "tokenizer_class": "PreTrainedTokenizerFast",