LeoChiuu
/

all-MiniLM-L6-v2-negations

@@ -6,40 +6,43 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:75
 - loss:CosineSimilarityLoss
 base_model: sentence-transformers/all-MiniLM-L6-v2
 datasets: []
 widget:
-- source_sentence: The Turkish language is an official language of the republic.
   sentences:
-  - The problem of head-of-line blocking using Virtual Output Queues was discussed
-    in the paper.
-  - He had also written Sonnets in Urdu.
-  - The Turkish language is an official language of the republic, alongside the Greek
-    language.
-- source_sentence: Spinal locks and cervical locks are allowed and mandatory in IBJJF
-    Brazilian jiu-jitsu competitions.
   sentences:
-  - He was the head baseball coach at Louisiana State University.
-  - Spinal locks and cervical locks are forbidden in IBJJF Brazilian jiu-jitsu competitions.
-  - It must be taken at the start of the main meal.
-- source_sentence: The blood samples tested positive for sarin.
   sentences:
-  - The blood samples tested negative for sarin.
-  - It was an inexpensive piece, but I would still have expected better quality.
-  - Additionally, a church at San Lazaro in Orange Walk District suffered severe damage.
-- source_sentence: Several small groups claim to continue to practice this faith.
   sentences:
-  - He only extensively held a position of Visiting Lecturer at UCLA in 1967.
-  - Six other dams were unsuccessful that day, two were small and four were minor
-    in size.
-  - All tiny groups claim to continue to practice this faith.
-- source_sentence: The wings are diffuse with scales.
   sentences:
-  - The wings are pale greenish brown, diffused with blackish scales.
-  - The theory has been rejected by other researchers.
-  - His views were in a minority at the Westminster Assembly.
 pipeline_tag: sentence-similarity
 ---
@@ -93,9 +96,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("LeoChiuu/all-MiniLM-L6-v2-negations")
 # Run inference
 sentences = [
-    'The wings are diffuse with scales.',
-    'The wings are pale greenish brown, diffused with blackish scales.',
-    'His views were in a minority at the Westminster Assembly.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -150,19 +153,19 @@ You can finetune this model on your own dataset.
 #### Unnamed Dataset
-* 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: 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>It is just very clean when burned.</code>                                                 | <code>It is also very dirty when burned.</code>                                                | <code>0</code> |
-  | <code>The blood samples tested positive for sarin.</code>                                       | <code>The blood samples tested negative for sarin.</code>                                      | <code>0</code> |
-  | <code>The species is named in honor of the marriage of Sara Anderson and Malcolm Slaney.</code> | <code>The species is named in honor of the divorce of Sara Anderson and Malcolm Slaney.</code> | <code>0</code> |
 * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
   ```json
   {
@@ -289,6 +292,107 @@ You can finetune this model on your own dataset.
 </details>
 ### Framework Versions
 - Python: 3.11.9
 - Sentence Transformers: 3.0.1

 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
+- dataset_size:77376
 - loss:CosineSimilarityLoss
 base_model: sentence-transformers/all-MiniLM-L6-v2
 datasets: []
 widget:
+- source_sentence: He has published several books on nutrition, trace metals but not
+    biochemistry imbalances.
   sentences:
+  - This in turn can help in effective communication between healthcare providers
+    and their patients.
+  - He has written several books on nutrition, trace metals, and biochemistry imbalances.
+  - One of the most boring movies I have ever seen.
+- source_sentence: She was denied the 2011 NSK Neustadt Prize for Children's Literature.
   sentences:
+  - She was the recipient of the 2011 NSK Neustadt Prize for Children's Literature.
+  - The ancient woodland at Dickshills is also located here.
+  - An element (such as a tree) that contributes to evapotranspiration can be called
+    an evapotranspirator.
+- source_sentence: Viking, after the resemblance the pitchers bear to the prow of
+    a Viking ship.
   sentences:
+  - Viking, after the striking difference the pitchers bear to the prow of a Viking
+    ship.
+  - Honshu is formed from the island arcs.
+  - For instance, even alcohol consumption by a pregnant woman is unable to lead to
+    fetal alcohol syndrome.
+- source_sentence: Logging has not been undertake near the headwaters of the creek.
   sentences:
+  - Then I had to continue pairing it periodically since it somehow kept dropping.
+  - That's fair, Nance.
+  - Logging has been done near the headwaters of the creek.
+- source_sentence: He published a history of Cornwall, New York in 1873.
   sentences:
+  - He failed to publish a history of Cornwall, New York in 1873.
+  - Salafis assert that reliance on taqlid has led to Islam 's decline.
+  - 'Lot of holes in the plot: there''s nothing about how he became the emperor; nothing
+    about where he spend 20 years between his childhood and mature age.'
 pipeline_tag: sentence-similarity
 ---
 model = SentenceTransformer("LeoChiuu/all-MiniLM-L6-v2-negations")
 # Run inference
 sentences = [
+    'He published a history of Cornwall, New York in 1873.',
+    'He failed to publish a history of Cornwall, New York in 1873.',
+    "Salafis assert that reliance on taqlid has led to Islam 's decline.",
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
 #### Unnamed Dataset
+* Size: 77,376 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: 6 tokens</li><li>mean: 16.2 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.32 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~53.20%</li><li>1: ~46.80%</li></ul> |
 * Samples:
+  | sentence_0                                                                            | sentence_1                                                                                       | label          |
+  |:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
+  | <code>The situation in Yemen was already much better than it was in Bahrain.</code>   | <code>The situation in Yemen was not much better than Bahrain.</code>                            | <code>0</code> |
+  | <code>She was a member of the Gamma Theta Upsilon honour society of geography.</code> | <code>She was denied membership of the Gamma Theta Upsilon honour society of mathematics.</code> | <code>0</code> |
+  | <code>Which aren't small and not worth the price.</code>                              | <code>Which are small and not worth the price.</code>                                            | <code>0</code> |
 * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
   ```json
   {
 </details>
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.1034 | 500   | 0.3382        |
+| 0.2068 | 1000  | 0.2112        |
+| 0.3102 | 1500  | 0.1649        |
+| 0.4136 | 2000  | 0.1454        |
+| 0.5170 | 2500  | 0.1244        |
+| 0.6203 | 3000  | 0.1081        |
+| 0.7237 | 3500  | 0.0962        |
+| 0.8271 | 4000  | 0.0924        |
+| 0.9305 | 4500  | 0.0852        |
+| 1.0339 | 5000  | 0.0812        |
+| 1.1373 | 5500  | 0.0833        |
+| 1.2407 | 6000  | 0.0736        |
+| 1.3441 | 6500  | 0.0756        |
+| 1.4475 | 7000  | 0.0665        |
+| 1.5509 | 7500  | 0.0661        |
+| 1.6543 | 8000  | 0.0625        |
+| 1.7577 | 8500  | 0.0621        |
+| 1.8610 | 9000  | 0.0593        |
+| 1.9644 | 9500  | 0.054         |
+| 2.0678 | 10000 | 0.0569        |
+| 2.1712 | 10500 | 0.0566        |
+| 2.2746 | 11000 | 0.0502        |
+| 2.3780 | 11500 | 0.0516        |
+| 2.4814 | 12000 | 0.0455        |
+| 2.5848 | 12500 | 0.0454        |
+| 2.6882 | 13000 | 0.0424        |
+| 2.7916 | 13500 | 0.044         |
+| 2.8950 | 14000 | 0.0376        |
+| 2.9983 | 14500 | 0.0386        |
+| 3.1017 | 15000 | 0.0392        |
+| 3.2051 | 15500 | 0.0344        |
+| 3.3085 | 16000 | 0.0348        |
+| 3.4119 | 16500 | 0.0343        |
+| 3.5153 | 17000 | 0.0322        |
+| 3.6187 | 17500 | 0.0324        |
+| 3.7221 | 18000 | 0.0278        |
+| 3.8255 | 18500 | 0.0294        |
+| 3.9289 | 19000 | 0.0292        |
+| 4.0323 | 19500 | 0.0276        |
+| 4.1356 | 20000 | 0.0285        |
+| 4.2390 | 20500 | 0.026         |
+| 4.3424 | 21000 | 0.0271        |
+| 4.4458 | 21500 | 0.0248        |
+| 4.5492 | 22000 | 0.0245        |
+| 4.6526 | 22500 | 0.0253        |
+| 4.7560 | 23000 | 0.022         |
+| 4.8594 | 23500 | 0.0219        |
+| 4.9628 | 24000 | 0.0207        |
+| 5.0662 | 24500 | 0.0212        |
+| 5.1696 | 25000 | 0.0218        |
+| 5.2730 | 25500 | 0.0192        |
+| 5.3763 | 26000 | 0.0198        |
+| 5.4797 | 26500 | 0.0183        |
+| 5.5831 | 27000 | 0.02          |
+| 5.6865 | 27500 | 0.0176        |
+| 5.7899 | 28000 | 0.0184        |
+| 5.8933 | 28500 | 0.0157        |
+| 5.9967 | 29000 | 0.0175        |
+| 6.1001 | 29500 | 0.0175        |
+| 6.2035 | 30000 | 0.0163        |
+| 6.3069 | 30500 | 0.0173        |
+| 6.4103 | 31000 | 0.0165        |
+| 6.5136 | 31500 | 0.0152        |
+| 6.6170 | 32000 | 0.0155        |
+| 6.7204 | 32500 | 0.0132        |
+| 6.8238 | 33000 | 0.0147        |
+| 6.9272 | 33500 | 0.0145        |
+| 7.0306 | 34000 | 0.014         |
+| 7.1340 | 34500 | 0.0147        |
+| 7.2374 | 35000 | 0.0126        |
+| 7.3408 | 35500 | 0.0141        |
+| 7.4442 | 36000 | 0.0127        |
+| 7.5476 | 36500 | 0.0132        |
+| 7.6510 | 37000 | 0.0125        |
+| 7.7543 | 37500 | 0.0111        |
+| 7.8577 | 38000 | 0.011         |
+| 7.9611 | 38500 | 0.0125        |
+| 8.0645 | 39000 | 0.0128        |
+| 8.1679 | 39500 | 0.013         |
+| 8.2713 | 40000 | 0.0115        |
+| 8.3747 | 40500 | 0.0111        |
+| 8.4781 | 41000 | 0.0108        |
+| 8.5815 | 41500 | 0.012         |
+| 8.6849 | 42000 | 0.0108        |
+| 8.7883 | 42500 | 0.0105        |
+| 8.8916 | 43000 | 0.0092        |
+| 8.9950 | 43500 | 0.0115        |
+| 9.0984 | 44000 | 0.0112        |
+| 9.2018 | 44500 | 0.0096        |
+| 9.3052 | 45000 | 0.0106        |
+| 9.4086 | 45500 | 0.011         |
+| 9.5120 | 46000 | 0.01          |
+| 9.6154 | 46500 | 0.011         |
+| 9.7188 | 47000 | 0.0097        |
+| 9.8222 | 47500 | 0.0096        |
+| 9.9256 | 48000 | 0.0102        |
 ### Framework Versions
 - Python: 3.11.9
 - Sentence Transformers: 3.0.1

model.safetensors CHANGED Viewed

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