mohamed20-AI/model_title

README.md

@@ -10,36 +10,37 @@ tags:
 - loss:CoSENTLoss
 base_model: abdeljalilELmajjodi/model
 widget:
-- 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:
-  - They are walking with a sign.
-  - A married couple is sleeping.
-  - There are children present
-- 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 child with mom and dad, on summer vacation at the beach.
-  - A person is outdoors, on a horse.
-  - The woman is wearing white.
 - source_sentence: 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.
   sentences:
-  - Two adults swimming in water
-  - A couple watch a little girl play by herself on the beach.
-  - Near a couple of restaurants, two people walk across the street.
-- 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.
+  - Two people ride bicycles into a tunnel.
+  - There are people just getting on a train
+  - There are children present
+- source_sentence: A man with blond-hair, and a brown shirt drinking out of a public
+    water fountain.
+  sentences:
+  - Some women are hugging on vacation.
+  - The family is sitting down for dinner.
+  - A blond man wearing a brown shirt is reading a book on a bench in the park
+- source_sentence: Two women who just had lunch hugging and saying goodbye.
+  sentences:
+  - There are two woman in this picture.
+  - Two adults run across the street to get away from a red shirted person chasing
+    them.
+  - The woman is wearing black.
+- source_sentence: A woman in a green jacket and hood over her head looking towards
+    a valley.
   sentences:
-  - They are working for John's Pizza.
-  - Two adults walking across a road near the convicted prisoner dressed in red
-  - Women are waiting by a tram.
-- source_sentence: A man, woman, and child enjoying themselves on a beach.
+  - The woman is wearing green.
+  - A woman in white.
+  - A man is drinking juice.
+- source_sentence: An older man sits with his orange juice at a small table in a coffee
+    shop while employees in bright colored shirts smile in the background.
   sentences:
-  - A team is trying to tag a runner out.
-  - There are women showing affection.
-  - A family of three is at the mall shopping.
+  - They are protesting outside the capital.
+  - A couple are playing frisbee with a young child at the beach.
+  - A boy flips a burger.
 datasets:
 - sentence-transformers/all-nli
 pipeline_tag: sentence-similarity
@@ -58,10 +59,10 @@ model-index:
       type: pair-score-evaluator-dev
     metrics:
     - type: pearson_cosine
-      value: 0.01358701091758253
+      value: -0.12381534704198764
       name: Pearson Cosine
     - type: spearman_cosine
-      value: 0.02861316917596507
+      value: -0.06398099132915955
       name: Spearman Cosine
 ---
 
@@ -115,9 +116,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
-    'A man, woman, and child enjoying themselves on a beach.',
-    'A family of three is at the mall shopping.',
-    'A team is trying to tag a runner out.',
+    'An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background.',
+    'A boy flips a burger.',
+    'They are protesting outside the capital.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -164,8 +165,8 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| pearson_cosine      | 0.0136     |
-| **spearman_cosine** | **0.0286** |
+| pearson_cosine      | -0.1238    |
+| **spearman_cosine** | **-0.064** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -189,16 +190,16 @@ You can finetune this model on your own dataset.
 * Size: 80 training samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
 * Approximate statistics based on the first 80 samples:
-  |         | sentence1                                                                          | sentence2                                                                         | score                                                          |
-  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                            | float                                                          |
-  | details | <ul><li>min: 10 tokens</li><li>mean: 26.15 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.68 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> |
+  |         | sentence1                                                                          | sentence2                                                                        | score                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                           | float                                                          |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 25.34 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.2 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>Two women, holding food carryout containers, hug.</code>                                                                       | <code>Two groups of rival gang members flipped each other off.</code> | <code>0.0</code> |
-  | <code>A man and a woman cross the street in front of a pizza and gyro restaurant.</code>                                             | <code>The people are standing still on the curb.</code>               | <code>0.0</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>The woman is waiting for a friend.</code>                       | <code>0.5</code> |
+  | sentence1                                                                                                                                                                                                      | sentence2                                               | score            |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------|:-----------------|
+  | <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>Some people board a train.</code>                 | <code>0.0</code> |
+  | <code>A few people in a restaurant setting, one of them is drinking orange juice.</code>                                                                                                                       | <code>The people are sitting at desks in school.</code> | <code>0.0</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 hosts a basketball game.</code>          | <code>0.0</code> |
 * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
   ```json
   {
@@ -215,16 +216,16 @@ You can finetune this model on your own dataset.
 * Size: 20 evaluation samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
 * Approximate statistics based on the first 20 samples:
-  |         | sentence1                                                                          | sentence2                                                                        | score                                                          |
-  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                           | float                                                          |
-  | details | <ul><li>min: 10 tokens</li><li>mean: 24.05 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.2 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.62</li><li>max: 1.0</li></ul> |
+  |         | sentence1                                                                         | sentence2                                                                        | score                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | float                                                         |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 27.3 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.1 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
 * Samples:
-  | sentence1                                                           | sentence2                                                               | score            |
-  |:--------------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------|
-  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code>                          | <code>1.0</code> |
-  | <code>Children smiling and waving at camera</code>                  | <code>There are children present</code>                                 | <code>1.0</code> |
-  | <code>A couple playing with a little boy on the beach.</code>       | <code>A couple watch a little girl play by herself on the beach.</code> | <code>0.0</code> |
+  | sentence1                                                                                                                            | sentence2                                                                  | score            |
+  |:-------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-----------------|
+  | <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 is wearing black.</code>                                   | <code>0.0</code> |
+  | <code>A couple play in the tide with their young son.</code>                                                                         | <code>The family is sitting down for dinner.</code>                        | <code>0.0</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> |
 * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
   ```json
   {
@@ -310,6 +311,7 @@ You can finetune this model on your own dataset.
 - `fsdp`: []
 - `fsdp_min_num_params`: 0
 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
 - `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
@@ -367,17 +369,17 @@ 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      | 2.5069        | -               | -                                        |
-| 0.5     | 5      | 3.0496        | -               | -                                        |
-| **1.0** | **10** | **3.0534**    | **2.7607**      | **0.0286**                               |
+| 0.1     | 1      | 3.0033        | -               | -                                        |
+| 0.5     | 5      | 2.987         | -               | -                                        |
+| **1.0** | **10** | **3.0908**    | **2.6311**      | **-0.064**                               |
 
 * The bold row denotes the saved checkpoint.
 
 ### Framework Versions
 - Python: 3.11.12
 - Sentence Transformers: 4.1.0
-- Transformers: 4.52.3
-- PyTorch: 2.7.0+cu126
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
 - Accelerate: 1.6.0
 - Datasets: 3.6.0
 - Tokenizers: 0.21.1

config_sentence_transformers.json

@@ -1,8 +1,8 @@
 {
   "__version__": {
     "sentence_transformers": "4.1.0",
-    "transformers": "4.52.3",
-    "pytorch": "2.7.0+cu126"
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
   },
   "prompts": {},
   "default_prompt_name": null,