Embedding/hack_ai_embbedding_model

README.md

@@ -8,37 +8,34 @@ tags:
 - loss:CoSENTLoss
 base_model: abdeljalilELmajjodi/model
 widget:
-- source_sentence: A woman in a green jacket and hood over her head looking towards
-    a valley.
-  sentences:
-  - The woman is cold.
-  - A person eating.
-  - A woman in white.
 - source_sentence: Woman in white in foreground and a man slightly behind walking
     with a sign for John's Pizza and Gyro in the background.
   sentences:
-  - A man is drinking juice.
-  - a woman eating a banana crosses a street
-  - The woman and man are outdoors.
+  - A man and a soman are eating together at John's Pizza and Gyro.
+  - A high school is hosting an event.
+  - A family of three is at the beach.
 - source_sentence: Woman in white in foreground and a man slightly behind walking
     with a sign for John's Pizza and Gyro in the background.
   sentences:
-  - Olympic swimming.
-  - A couple are playing with a young child outside.
-  - A female is next to a man.
-- source_sentence: Woman in white in foreground and a man slightly behind walking
-    with a sign for John's Pizza and Gyro in the background.
+  - A married couple is walking next to each other.
+  - A married couple is sleeping.
+  - They are working for John's Pizza.
+- source_sentence: A boy is jumping on skateboard in the middle of a red bridge.
+  sentences:
+  - The boy skates down the sidewalk.
+  - The woman is wearing black.
+  - An elderly man sits in a small shop.
+- source_sentence: Two women who just had lunch hugging and saying goodbye.
   sentences:
-  - A man and a woman walk down a crowded city street.
-  - A couple are playing frisbee with a young child at the beach.
-  - Two people walk away from a restaurant across a street.
-- source_sentence: A woman is walking across the street eating a banana, while a man
-    is following with his briefcase.
+  - There are two woman in this picture.
+  - Two adults walk across a street.
+  - A woman ordering pizza.
+- source_sentence: High fashion ladies wait outside a tram beside a crowd of people
+    in the city.
   sentences:
-  - A boy flips a burger.
-  - The family is on vacation.
-  - A woman eats a banana and walks across a street, and there is a man trailing behind
-    her.
+  - A blond man getting a drink of water from a fountain in the park.
+  - A person is at a diner, ordering an omelette.
+  - The women do not care what clothes they wear.
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -55,10 +52,10 @@ model-index:
       type: pair-score-evaluator-dev
     metrics:
     - type: pearson_cosine
-      value: 0.2996981954365559
+      value: 0.03573386095548956
       name: Pearson Cosine
     - type: spearman_cosine
-      value: 0.45480374122323747
+      value: 0.0572850816078118
       name: Spearman Cosine
 ---
 
@@ -112,9 +109,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'A woman is walking across the street eating a banana, while a man is following with his briefcase.',
-    'A woman eats a banana and walks across a street, and there is a man trailing behind her.',
-    'A boy flips a burger.',
+    'High fashion ladies wait outside a tram beside a crowd of people in the city.',
+    'The women do not care what clothes they wear.',
+    'A blond man getting a drink of water from a fountain in the park.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -123,9 +120,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.9968, 0.9809],
-#         [0.9968, 1.0000, 0.9792],
-#         [0.9809, 0.9792, 1.0000]])
+# tensor([[1.0000, 0.9959, 0.9963],
+#         [0.9959, 1.0000, 0.9936],
+#         [0.9963, 0.9936, 1.0000]])
 ```
 <!--
 ### Direct Usage (Transformers)
@@ -162,8 +159,8 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| pearson_cosine      | 0.2997     |
-| **spearman_cosine** | **0.4548** |
+| pearson_cosine      | 0.0357     |
+| **spearman_cosine** | **0.0573** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -190,13 +187,13 @@ You can finetune this model on your own dataset.
   |         | sentence1                                                                          | sentence2                                                                        | score                                                          |
   |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
   | type    | string                                                                             | string                                                                           | float                                                          |
-  | details | <ul><li>min: 10 tokens</li><li>mean: 25.23 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.0 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 24.73 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.0 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
 * Samples:
-  | sentence1                                                                                                                                                                                                      | sentence2                                                     | score            |
-  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:-----------------|
-  | <code>A Little League team tries to catch a runner sliding into a base in an afternoon game.</code>                                                                                                            | <code>A team is trying to score the games winning out.</code> | <code>0.5</code> |
-  | <code>Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.</code> | <code>Two adults walk across the street.</code>               | <code>1.0</code> |
-  | <code>High fashion ladies wait outside a tram beside a crowd of people in the city.</code>                                                                                                                     | <code>The women enjoy having a good fashion sense.</code>     | <code>0.5</code> |
+  | sentence1                                                                                                                                | sentence2                                                                  | score            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-----------------|
+  | <code>A couple playing with a little boy on the beach.</code>                                                                            | <code>A couple are playing frisbee with a young child at the beach.</code> | <code>0.5</code> |
+  | <code>A Little League team tries to catch a runner sliding into a base in an afternoon game.</code>                                      | <code>A team is trying to score the games winning out.</code>              | <code>0.5</code> |
+  | <code>The school is having a special event in order to show the american culture on how other cultures are dealt with in parties.</code> | <code>A school is hosting an event.</code>                                 | <code>1.0</code> |
 * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
   ```json
   {
@@ -216,13 +213,13 @@ You can finetune this model on your own dataset.
   |         | sentence1                                                                          | sentence2                                                                        | score                                                          |
   |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
   | type    | string                                                                             | string                                                                           | float                                                          |
-  | details | <ul><li>min: 13 tokens</li><li>mean: 27.75 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.9 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.62</li><li>max: 1.0</li></ul> |
+  | details | <ul><li>min: 16 tokens</li><li>mean: 29.75 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 11.9 tokens</li><li>max: 20 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 couple play in the tide with their young son.</code>                                                                         | <code>The family is on vacation.</code>                                    | <code>0.5</code> |
-  | <code>A couple playing with a little boy on the beach.</code>                                                                        | <code>A couple are playing frisbee with a young child at the beach.</code> | <code>0.5</code> |
-  | <code>Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.</code> | <code>The woman and man are outdoors.</code>                               | <code>1.0</code> |
+  | sentence1                                                                                                                                                                                                      | sentence2                                                  | score            |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|:-----------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>                                                                                                                                            | <code>A person is at a diner, ordering an omelette.</code> | <code>0.0</code> |
+  | <code>Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.</code> | <code>Two adults walk across a street.</code>              | <code>1.0</code> |
+  | <code>Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.</code>                                                                           | <code>They are working for John's Pizza.</code>            | <code>0.5</code> |
 * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
   ```json
   {
@@ -347,14 +344,14 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch   | Step   | Training Loss | Validation Loss | pair-score-evaluator-dev_spearman_cosine |
 |:-------:|:------:|:-------------:|:---------------:|:----------------------------------------:|
-| 0.1     | 1      | 2.8785        | -               | -                                        |
-| 0.5     | 5      | 3.0593        | -               | -                                        |
-| **1.0** | **10** | **3.117**     | **2.6209**      | **0.4548**                               |
+| 0.1     | 1      | 2.8066        | -               | -                                        |
+| 0.5     | 5      | 3.3184        | -               | -                                        |
+| **1.0** | **10** | **3.1168**    | **2.7511**      | **0.0573**                               |
 
 * The bold row denotes the saved checkpoint.
 
 ### Training Time
-- **Training**: 4.6 minutes
+- **Training**: 4.0 minutes
 
 ### Framework Versions
 - Python: 3.12.13