Add new SentenceTransformer model.

README.md

@@ -6,42 +6,43 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:38688
-- loss:ContrastiveLoss
+- dataset_size:75
+- loss:CosineSimilarityLoss
 base_model: sentence-transformers/all-MiniLM-L6-v2
 datasets: []
 widget:
-- source_sentence: There is a heavy cost for this service provided in conjunction
-    with NOAA and SARSAT.
+- source_sentence: This store featured in the SavaCentre TV adverts in 1983.
   sentences:
-  - No significant changes have been made to the roadway except for its legal definition.
-  - Some academics have questioned the ethics of these payments.
-  - There is no charge for this service provided in conjunction with NOAA and SARSAT.
-- source_sentence: You're not thin.
+  - I love the Scream movies and all horror movies and this one ranks way up there.
+  - Development of synchronous toothed-belts was halted by the Gilmer company prior
+    to 1940.
+  - This store was not featured in the SavaCentre TV promotions in 1983.
+- source_sentence: In 2014, Nextgen earns KLAS Top Performance Honors for Ambulatory
+    RCM Services.
   sentences:
-  - This process is called low-dimensional embedded in machine learning.
-  - You're thin.
-  - Jean Prouvost was the founder of Marie Claire.
-- source_sentence: The lead man is charisma-free.
+  - These strategies employ reporter transposon s and in vitro expression technology
+    (IVET).
+  - In 2014, Nextgen fails to achieve KLAS Top Performance Honors for Ambulatory RCM
+    Services.
+  - The film's sole bright spot was Jonah Hill (who will look almost unrecognizable
+    to fans of the recent Superbad due to the amount of weight he lost in the interim).
+- source_sentence: E105 has never been implicated in atopic asthma.
   sentences:
-  - Fossil egg s are rare, but one oogenus, Polyclonoolithus, was discovered in the
-    Hekou Group.
-  - The roof is shingled, and topped by a small belfry.
-  - The lead man doesn't have charisma.
-- source_sentence: Willis has criticized the rules adopted by the RNC, particularly
-    Rules 12, 16, and 40.
+  - E105 has been implicated in non-atopic asthma.
+  - The species is named in honor of the divorce of Sara Anderson and Malcolm Slaney.
+  - Each annex to a filed document is not required to have page numbering.
+- source_sentence: Additionally, a church at San Lazaro in Orange Walk District escaped
+    all damage.
   sentences:
-  - Willis has fully accepted the rules adopted by the RNC, particularly Rules 12,
-    16, and 40.
-  - I can't stop reading.
-  - This force acts on water independently of the wind stress.
-- source_sentence: The publication was named after Sir James Joynton Smith.
+  - Kuwait has a reputation for being the central music influence of the GCC countries.
+  - Early settlers may have introduced it 4,000 years ago.
+  - Additionally, a church at San Lazaro in Orange Walk District suffered severe damage.
+- source_sentence: The content in Australia is lower than in other reports.
   sentences:
-  - Detailed specific information on the ongoing validation activities is being made
-    available in related publications.
-  - On November 25, 2012, Tom O'Brien was reinstated.
-  - The publication took its name from its founder and chief financer Sir James Joynton
-    Smith.
+  - Other reports also show a content lower than 0.1% in Australia.
+  - Commercial DNP is unable to be utilized as an antiseptic or as a non-selective
+    bioaccumulating pesticide.
+  - Installation of Halon systems is mandated by the European Union.
 pipeline_tag: sentence-similarity
 ---
 
@@ -95,9 +96,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("LeoChiuu/all-MiniLM-L6-v2-negations")
 # Run inference
 sentences = [
-    'The publication was named after Sir James Joynton Smith.',
-    'The publication took its name from its founder and chief financer Sir James Joynton Smith.',
-    "On November 25, 2012, Tom O'Brien was reinstated.",
+    'The content in Australia is lower than in other reports.',
+    'Other reports also show a content lower than 0.1% in Australia.',
+    'Installation of Halon systems is mandated by the European Union.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -152,25 +153,23 @@ You can finetune this model on your own dataset.
 #### Unnamed Dataset
 
 
-* Size: 38,688 training samples
+* Size: 75 training samples
 * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence_0                                                                        | sentence_1                                                                        | label                                           |
   |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
   | type    | string                                                                            | string                                                                            | int                                             |
-  | details | <ul><li>min: 5 tokens</li><li>mean: 15.94 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.96 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>0: ~48.50%</li><li>1: ~51.50%</li></ul> |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 16.36 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.55 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~61.33%</li><li>1: ~38.67%</li></ul> |
 * Samples:
-  | sentence_0                                                                        | sentence_1                                                                        | label          |
-  |:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
-  | <code>No, that is impossible.</code>                                              | <code>No, that is not possible.</code>                                            | <code>0</code> |
-  | <code>The building did indeed serve as a hof, according to the bone finds.</code> | <code>The bone finds thus indicate the building did indeed serve as a hof.</code> | <code>0</code> |
-  | <code>The building became a pet shop.</code>                                      | <code>The building became a hospital.</code>                                      | <code>1</code> |
-* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  | sentence_0                                                                                   | sentence_1                                                                                | label          |
+  |:---------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------|
+  | <code>It wasn't an inexpensive piece, but I would still have expected better quality.</code> | <code>It was an inexpensive piece, but I would still have expected better quality.</code> | <code>0</code> |
+  | <code>My name is noncrucial.</code>                                                          | <code>My name is important.</code>                                                        | <code>0</code> |
+  | <code>Hawthorne mostly wrote against his own religious belief.</code>                        | <code>Hawthorne wrote against his beliefs.</code>                                         | <code>1</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
   ```json
   {
-      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
-      "margin": 0.5,
-      "size_average": true
+      "loss_fct": "torch.nn.modules.loss.MSELoss"
   }
   ```
 
@@ -293,59 +292,6 @@ You can finetune this model on your own dataset.
 
 </details>
 
-### Training Logs
-| Epoch  | Step  | Training Loss |
-|:------:|:-----:|:-------------:|
-| 0.2068 | 500   | 0.0353        |
-| 0.4136 | 1000  | 0.0307        |
-| 0.6203 | 1500  | 0.0234        |
-| 0.8271 | 2000  | 0.0187        |
-| 1.0339 | 2500  | 0.0152        |
-| 1.2407 | 3000  | 0.0134        |
-| 1.4475 | 3500  | 0.0123        |
-| 1.6543 | 4000  | 0.0111        |
-| 1.8610 | 4500  | 0.0107        |
-| 2.0678 | 5000  | 0.0097        |
-| 2.2746 | 5500  | 0.0096        |
-| 2.4814 | 6000  | 0.0091        |
-| 2.6882 | 6500  | 0.0087        |
-| 2.8950 | 7000  | 0.0086        |
-| 3.1017 | 7500  | 0.0075        |
-| 3.3085 | 8000  | 0.008         |
-| 3.5153 | 8500  | 0.0074        |
-| 3.7221 | 9000  | 0.007         |
-| 3.9289 | 9500  | 0.007         |
-| 4.1356 | 10000 | 0.0063        |
-| 4.3424 | 10500 | 0.0068        |
-| 4.5492 | 11000 | 0.0061        |
-| 4.7560 | 11500 | 0.0059        |
-| 4.9628 | 12000 | 0.0056        |
-| 5.1696 | 12500 | 0.0052        |
-| 5.3763 | 13000 | 0.0055        |
-| 5.5831 | 13500 | 0.0051        |
-| 5.7899 | 14000 | 0.005         |
-| 5.9967 | 14500 | 0.0047        |
-| 6.2035 | 15000 | 0.0046        |
-| 6.4103 | 15500 | 0.0047        |
-| 6.6170 | 16000 | 0.0044        |
-| 6.8238 | 16500 | 0.0044        |
-| 7.0306 | 17000 | 0.0041        |
-| 7.2374 | 17500 | 0.004         |
-| 7.4442 | 18000 | 0.0044        |
-| 7.6510 | 18500 | 0.0039        |
-| 7.8577 | 19000 | 0.0038        |
-| 8.0645 | 19500 | 0.0038        |
-| 8.2713 | 20000 | 0.0037        |
-| 8.4781 | 20500 | 0.0039        |
-| 8.6849 | 21000 | 0.0037        |
-| 8.8916 | 21500 | 0.0036        |
-| 9.0984 | 22000 | 0.0034        |
-| 9.3052 | 22500 | 0.0036        |
-| 9.5120 | 23000 | 0.0035        |
-| 9.7188 | 23500 | 0.0034        |
-| 9.9256 | 24000 | 0.0035        |
-
-
 ### Framework Versions
 - Python: 3.11.9
 - Sentence Transformers: 3.0.1
@@ -372,20 +318,6 @@ You can finetune this model on your own dataset.
 }
 ```
 
-#### ContrastiveLoss
-```bibtex
-@inproceedings{hadsell2006dimensionality,
-    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
-    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
-    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
-    year={2006},
-    volume={2},
-    number={},
-    pages={1735-1742},
-    doi={10.1109/CVPR.2006.100}
-}
-```
-
 <!--
 ## Glossary
 

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "LeoChiuu/all-MiniLM-L6-v2-negations",
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
   "architectures": [
     "BertModel"
   ],

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6b68ced173371fb910f0fa0d901c4c1cf752167e673dd1c7a014d80b80d7410a
+oid sha256:2b3f93fc93c0fbdf4be9f9217841543915515a6610212538520a608457a9d4a7
 size 90864192