Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,1202 @@
+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:1K<n<10K
+- loss:MatryoshkaLoss
+- loss:CoSENTLoss
+base_model: distilbert/distilbert-base-uncased
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+widget:
+- source_sentence: A plane in the sky.
+  sentences:
+  - Two airplanes in the sky.
+  - Two women are sitting in a cafe.
+  - Turkey's PM Warns Against Protests
+- source_sentence: A man jumping rope
+  sentences:
+  - A man climbs a rope.
+  - Blast on Indian train kills one
+  - Israel expands subsidies to settlements
+- source_sentence: A baby is laughing.
+  sentences:
+  - The baby laughed in his car seat.
+  - The girl is playing the guitar.
+  - Bangladesh Islamist leader executed
+- source_sentence: A plane is landing.
+  sentences:
+  - A animated airplane is landing.
+  - A man plays an acoustic guitar.
+  - Obama urges no new sanctions on Iran
+- source_sentence: A boy is vacuuming.
+  sentences:
+  - A little boy is vacuuming the floor.
+  - Suicide bomber strikes in Syria
+  - 32 die in Bangladesh protest
+pipeline_tag: sentence-similarity
+model-index:
+- name: SentenceTransformer based on distilbert/distilbert-base-uncased
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 768
+      type: sts-dev-768
+    metrics:
+    - type: pearson_cosine
+      value: 0.8580007118837358
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.871820299536176
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8579597824452743
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8611676230134329
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8584693242993966
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8617539394714434
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.6259192943899555
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6245849846631494
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8584693242993966
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.871820299536176
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 512
+      type: sts-dev-512
+    metrics:
+    - type: pearson_cosine
+      value: 0.855328467168775
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8708546925464771
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8571701704416792
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8609603329646862
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8577665956034857
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8611867637483455
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.6301839390729895
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6312551259723912
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8577665956034857
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8708546925464771
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 256
+      type: sts-dev-256
+    metrics:
+    - type: pearson_cosine
+      value: 0.8534192140857989
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8684742287834586
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8550376893582918
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8595873940460774
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.855243500036296
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8595389790366662
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.5692600956239565
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.5631798664802073
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.855243500036296
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8684742287834586
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 128
+      type: sts-dev-128
+    metrics:
+    - type: pearson_cosine
+      value: 0.8437376978373121
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8634082420330794
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8454596574177755
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.85188111210432
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8479887421152008
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8537259447832961
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.5513203019384504
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.5500687993669725
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8479887421152008
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8634082420330794
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 64
+      type: sts-dev-64
+    metrics:
+    - type: pearson_cosine
+      value: 0.8272184719216283
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8541030591238341
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8307462071466211
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8406982840852595
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8342382781891662
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8427338906559259
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.494520518114596
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.49218360841938574
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8342382781891662
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8541030591238341
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 32
+      type: sts-dev-32
+    metrics:
+    - type: pearson_cosine
+      value: 0.795037446434113
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8337679875014413
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8120635303724889
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8249212312847407
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8157607542813738
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8262833782950811
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.44442829473227297
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.4333209339301445
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8157607542813738
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8337679875014413
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts dev 16
+      type: sts-dev-16
+    metrics:
+    - type: pearson_cosine
+      value: 0.7402920507586056
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7953398971914366
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.7661819958789702
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.7806209887724272
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.7753319460863385
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.788448392758016
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.2914268467178465
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.2731801701260987
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.7753319460863385
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.7953398971914366
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 768
+      type: sts-test-768
+    metrics:
+    - type: pearson_cosine
+      value: 0.8355126555886146
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8474343771835785
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8477769261693708
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8440487632905719
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8482353907773731
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8443357402859023
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.575155372226532
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.5645826036063977
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8482353907773731
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8474343771835785
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 512
+      type: sts-test-512
+    metrics:
+    - type: pearson_cosine
+      value: 0.8345636179092932
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.847969741682177
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8471375569231226
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8432315278152519
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8475673449165414
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8438566473590643
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.5890647647307824
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.579599198660516
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8475673449165414
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.847969741682177
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 256
+      type: sts-test-256
+    metrics:
+    - type: pearson_cosine
+      value: 0.8264268046184008
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8414784020776254
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8414377075419083
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8388634084489552
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8423455168447094
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8400797815114284
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.5229860109488433
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.5099269577284724
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8423455168447094
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8414784020776254
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 128
+      type: sts-test-128
+    metrics:
+    - type: pearson_cosine
+      value: 0.8189773000477083
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.837625236881656
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8349887918183595
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8336489133404312
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8365085956274743
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8347627903646608
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.49799738412782535
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.48970409354637134
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8365085956274743
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.837625236881656
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 64
+      type: sts-test-64
+    metrics:
+    - type: pearson_cosine
+      value: 0.8062259318483077
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.8292433269349447
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8236527010227455
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8243846152203906
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8273451113428331
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8269777736926925
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.4318247709105578
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.4325030690630689
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8273451113428331
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.8292433269349447
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 32
+      type: sts-test-32
+    metrics:
+    - type: pearson_cosine
+      value: 0.7769698706658718
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.813231133965274
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.8040659399939705
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.8083901845044422
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.8089540323890078
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.8126434700070444
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.3721968691924307
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.36359211044547146
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.8089540323890078
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.813231133965274
+      name: Spearman Max
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test 16
+      type: sts-test-16
+    metrics:
+    - type: pearson_cosine
+      value: 0.7350580362911046
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7811480253828886
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.7686995805327835
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.7767016091591996
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.7732639293607727
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.7798783495241994
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.25479413300114095
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.24117846955339683
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.7732639293607727
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.7811480253828886
+      name: Spearman Max
+---
+
+# SentenceTransformer based on distilbert/distilbert-base-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
+  (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})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("mrm8488/distilbert-base-matryoshka-sts-v2")
+# Run inference
+sentences = [
+    'A boy is vacuuming.',
+    'A little boy is vacuuming the floor.',
+    'Suicide bomber strikes in Syria',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-dev-768`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.858      |
+| **spearman_cosine** | **0.8718** |
+| pearson_manhattan   | 0.858      |
+| spearman_manhattan  | 0.8612     |
+| pearson_euclidean   | 0.8585     |
+| spearman_euclidean  | 0.8618     |
+| pearson_dot         | 0.6259     |
+| spearman_dot        | 0.6246     |
+| pearson_max         | 0.8585     |
+| spearman_max        | 0.8718     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-512`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8553     |
+| **spearman_cosine** | **0.8709** |
+| pearson_manhattan   | 0.8572     |
+| spearman_manhattan  | 0.861      |
+| pearson_euclidean   | 0.8578     |
+| spearman_euclidean  | 0.8612     |
+| pearson_dot         | 0.6302     |
+| spearman_dot        | 0.6313     |
+| pearson_max         | 0.8578     |
+| spearman_max        | 0.8709     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-256`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8534     |
+| **spearman_cosine** | **0.8685** |
+| pearson_manhattan   | 0.855      |
+| spearman_manhattan  | 0.8596     |
+| pearson_euclidean   | 0.8552     |
+| spearman_euclidean  | 0.8595     |
+| pearson_dot         | 0.5693     |
+| spearman_dot        | 0.5632     |
+| pearson_max         | 0.8552     |
+| spearman_max        | 0.8685     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-128`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8437     |
+| **spearman_cosine** | **0.8634** |
+| pearson_manhattan   | 0.8455     |
+| spearman_manhattan  | 0.8519     |
+| pearson_euclidean   | 0.848      |
+| spearman_euclidean  | 0.8537     |
+| pearson_dot         | 0.5513     |
+| spearman_dot        | 0.5501     |
+| pearson_max         | 0.848      |
+| spearman_max        | 0.8634     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-64`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8272     |
+| **spearman_cosine** | **0.8541** |
+| pearson_manhattan   | 0.8307     |
+| spearman_manhattan  | 0.8407     |
+| pearson_euclidean   | 0.8342     |
+| spearman_euclidean  | 0.8427     |
+| pearson_dot         | 0.4945     |
+| spearman_dot        | 0.4922     |
+| pearson_max         | 0.8342     |
+| spearman_max        | 0.8541     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-32`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.795      |
+| **spearman_cosine** | **0.8338** |
+| pearson_manhattan   | 0.8121     |
+| spearman_manhattan  | 0.8249     |
+| pearson_euclidean   | 0.8158     |
+| spearman_euclidean  | 0.8263     |
+| pearson_dot         | 0.4444     |
+| spearman_dot        | 0.4333     |
+| pearson_max         | 0.8158     |
+| spearman_max        | 0.8338     |
+
+#### Semantic Similarity
+* Dataset: `sts-dev-16`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.7403     |
+| **spearman_cosine** | **0.7953** |
+| pearson_manhattan   | 0.7662     |
+| spearman_manhattan  | 0.7806     |
+| pearson_euclidean   | 0.7753     |
+| spearman_euclidean  | 0.7884     |
+| pearson_dot         | 0.2914     |
+| spearman_dot        | 0.2732     |
+| pearson_max         | 0.7753     |
+| spearman_max        | 0.7953     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-768`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8355     |
+| **spearman_cosine** | **0.8474** |
+| pearson_manhattan   | 0.8478     |
+| spearman_manhattan  | 0.844      |
+| pearson_euclidean   | 0.8482     |
+| spearman_euclidean  | 0.8443     |
+| pearson_dot         | 0.5752     |
+| spearman_dot        | 0.5646     |
+| pearson_max         | 0.8482     |
+| spearman_max        | 0.8474     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-512`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value     |
+|:--------------------|:----------|
+| pearson_cosine      | 0.8346    |
+| **spearman_cosine** | **0.848** |
+| pearson_manhattan   | 0.8471    |
+| spearman_manhattan  | 0.8432    |
+| pearson_euclidean   | 0.8476    |
+| spearman_euclidean  | 0.8439    |
+| pearson_dot         | 0.5891    |
+| spearman_dot        | 0.5796    |
+| pearson_max         | 0.8476    |
+| spearman_max        | 0.848     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-256`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8264     |
+| **spearman_cosine** | **0.8415** |
+| pearson_manhattan   | 0.8414     |
+| spearman_manhattan  | 0.8389     |
+| pearson_euclidean   | 0.8423     |
+| spearman_euclidean  | 0.8401     |
+| pearson_dot         | 0.523      |
+| spearman_dot        | 0.5099     |
+| pearson_max         | 0.8423     |
+| spearman_max        | 0.8415     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-128`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.819      |
+| **spearman_cosine** | **0.8376** |
+| pearson_manhattan   | 0.835      |
+| spearman_manhattan  | 0.8336     |
+| pearson_euclidean   | 0.8365     |
+| spearman_euclidean  | 0.8348     |
+| pearson_dot         | 0.498      |
+| spearman_dot        | 0.4897     |
+| pearson_max         | 0.8365     |
+| spearman_max        | 0.8376     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-64`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.8062     |
+| **spearman_cosine** | **0.8292** |
+| pearson_manhattan   | 0.8237     |
+| spearman_manhattan  | 0.8244     |
+| pearson_euclidean   | 0.8273     |
+| spearman_euclidean  | 0.827      |
+| pearson_dot         | 0.4318     |
+| spearman_dot        | 0.4325     |
+| pearson_max         | 0.8273     |
+| spearman_max        | 0.8292     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-32`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.777      |
+| **spearman_cosine** | **0.8132** |
+| pearson_manhattan   | 0.8041     |
+| spearman_manhattan  | 0.8084     |
+| pearson_euclidean   | 0.809      |
+| spearman_euclidean  | 0.8126     |
+| pearson_dot         | 0.3722     |
+| spearman_dot        | 0.3636     |
+| pearson_max         | 0.809      |
+| spearman_max        | 0.8132     |
+
+#### Semantic Similarity
+* Dataset: `sts-test-16`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.7351     |
+| **spearman_cosine** | **0.7811** |
+| pearson_manhattan   | 0.7687     |
+| spearman_manhattan  | 0.7767     |
+| pearson_euclidean   | 0.7733     |
+| spearman_euclidean  | 0.7799     |
+| pearson_dot         | 0.2548     |
+| spearman_dot        | 0.2412     |
+| pearson_max         | 0.7733     |
+| spearman_max        | 0.7811     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### sentence-transformers/stsb
+
+* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
+* Size: 5,749 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                  | sentence2                                                             | score             |
+  |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
+  | <code>A plane is taking off.</code>                        | <code>An air plane is taking off.</code>                              | <code>1.0</code>  |
+  | <code>A man is playing a large flute.</code>               | <code>A man is playing a flute.</code>                                | <code>0.76</code> |
+  | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "CoSENTLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64,
+          32,
+          16
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### sentence-transformers/stsb
+
+* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
+* Size: 1,500 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                         | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            | float                                                          |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                         | sentence2                                             | score             |
+  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
+  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
+  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
+  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "CoSENTLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64,
+          32,
+          16
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `num_train_epochs`: 4
+- `warmup_ratio`: 0.1
+- `bf16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss    | sts-dev-128_spearman_cosine | sts-dev-16_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-32_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-16_spearman_cosine | sts-test-256_spearman_cosine | sts-test-32_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
+|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
+| 2.2222 | 100  | 60.4066       | 60.8718 | 0.8634                      | 0.7953                     | 0.8685                      | 0.8338                     | 0.8709                      | 0.8541                     | 0.8718                      | -                            | -                           | -                            | -                           | -                            | -                           | -                            |
+| 4.0    | 180  | -             | -       | -                           | -                          | -                           | -                          | -                           | -                          | -                           | 0.8376                       | 0.7811                      | 0.8415                       | 0.8132                      | 0.8480                       | 0.8292                      | 0.8474                       |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.0
+- Transformers: 4.41.1
+- PyTorch: 2.3.0+cu121
+- Accelerate: 0.30.1
+- Datasets: 2.19.1
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```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 = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "distilbert-base-uncased",
+  "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,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.0",
+    "transformers": "4.41.1",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06aba0ffaed6543b2727a847faf42f58252a42f2aaf442dbdb84fc02536bcd79
+size 265462608

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}