AdaptiveLayerLoss(model=model,

loss=train_loss,
n_layers_per_step = 1,
last_layer_weight = 1,
prior_layers_weight= 1,
kl_div_weight = 1,
kl_temperature= 1,
)''')
lr = 1e-6. batch = 42, schedule = cosine

README.md

@@ -7,7 +7,7 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:314315
+- dataset_size:67190
 - loss:AdaptiveLayerLoss
 - loss:MultipleNegativesRankingLoss
 base_model: microsoft/deberta-v3-small
@@ -49,34 +49,44 @@ metrics:
 - max_precision
 - max_recall
 - max_ap
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
 widget:
-- source_sentence: A man plays the violin.
+- source_sentence: A worker peers out from atop a building under construction.
   sentences:
-  - A man is playing violin.
-  - The back of a pig under a tree with a cow in the background.
-  - The plane is getting ready to take off.
-- source_sentence: A person drops a camera down an escelator.
+  - The man pleads for mercy.
+  - People and a baby crossing the street.
+  - A person is atop of a building.
+- source_sentence: An aisle at Best Buy with an employee standing at the computer
+    and a Geek Squad sign in the background.
   sentences:
-  - Something is bothering your cat and he does not like it.
-  - A man tosses a bag down an escalator.
-  - Two smiling women holding a baby.
-- source_sentence: One football player tries to tackle a player on the opposing team.
+  - the man is watching the stars
+  - The employee is wearing a blue shirt.
+  - A person balancing.
+- source_sentence: A man with a long white beard is examining a camera and another
+    man with a black shirt is in the background.
   sentences:
-  - I think Stephen King's comments are helpful in this regard.
-  - Our interactions are merely depends on where we put our perception.
-  - A football player attempts a tackle.
-- source_sentence: The two men are wearing jeans.
+  - a man is with another man
+  - Children in uniforms climb a tower.
+  - There are five children.
+- source_sentence: A black dog with a blue collar is jumping into the water.
   sentences:
-  - Four people eating dessert around a table.
-  - Here are some things that worked with my son who started toilet training around
-    2.5 years.
-  - The two men are wearing pants.
-- source_sentence: This may be overly obvious, but in American English, saying "you're
-    welcome" is certainly polite and standard.
+  - The dog is playing tug of war with a stick.
+  - There is a woman painting.
+  - A black dog wearing a blue collar is chasing something into the water.
+- source_sentence: A wet child stands in chest deep ocean water.
   sentences:
-  - I'm not sure how "Not at all" sounds in response to "thank you".
-  - As bikeboy389 said, you can learn a lot by looking at students' native languages.
-  - A laptop and a PC at a workstation.
+  - A woman paints a portrait of her best friend.
+  - A person in red is cutting the grass on a riding mower
+  - The child s playing on the beach.
 pipeline_tag: sentence-similarity
 model-index:
 - name: SentenceTransformer based on microsoft/deberta-v3-small
@@ -89,110 +99,147 @@ model-index:
       type: unknown
     metrics:
     - type: cosine_accuracy
-      value: 0.5397679884752445
+      value: 0.6583157259281618
       name: Cosine Accuracy
     - type: cosine_accuracy_threshold
-      value: 0.9089176654815674
+      value: 0.6766541004180908
       name: Cosine Accuracy Threshold
     - type: cosine_f1
-      value: 0.6834040429248815
+      value: 0.7049362860324137
       name: Cosine F1
     - type: cosine_f1_threshold
-      value: 0.3752323389053345
+      value: 0.6017583012580872
       name: Cosine F1 Threshold
     - type: cosine_precision
-      value: 0.5191082802547771
+      value: 0.6115046147241897
       name: Cosine Precision
     - type: cosine_recall
-      value: 0.9998539506353147
+      value: 0.8320677570093458
       name: Cosine Recall
     - type: cosine_ap
-      value: 0.5794582374804604
+      value: 0.6995030811464378
       name: Cosine Ap
     - type: dot_accuracy
-      value: 0.5302903935097429
+      value: 0.6272260790824027
       name: Dot Accuracy
     - type: dot_accuracy_threshold
-      value: 391.4422302246094
+      value: 163.25054931640625
       name: Dot Accuracy Threshold
     - type: dot_f1
-      value: 0.6834040429248815
+      value: 0.6976381461675579
       name: Dot F1
     - type: dot_f1_threshold
-      value: 175.07894897460938
+      value: 119.20779418945312
       name: Dot F1 Threshold
     - type: dot_precision
-      value: 0.5191082802547771
+      value: 0.5639409221902018
       name: Dot Precision
     - type: dot_recall
-      value: 0.9998539506353147
+      value: 0.914427570093458
       name: Dot Recall
     - type: dot_ap
-      value: 0.5621671154600225
+      value: 0.643747511442345
       name: Dot Ap
     - type: manhattan_accuracy
-      value: 0.5644855561452726
+      value: 0.6571083610021129
       name: Manhattan Accuracy
     - type: manhattan_accuracy_threshold
-      value: 160.045654296875
+      value: 243.75453186035156
       name: Manhattan Accuracy Threshold
     - type: manhattan_f1
-      value: 0.6834381551362683
+      value: 0.7055783910745744
       name: Manhattan F1
     - type: manhattan_f1_threshold
-      value: 322.75946044921875
+      value: 295.95947265625
       name: Manhattan F1 Threshold
     - type: manhattan_precision
-      value: 0.5191476454083567
+      value: 0.5900608917697898
       name: Manhattan Precision
     - type: manhattan_recall
-      value: 0.9998539506353147
+      value: 0.8773364485981309
       name: Manhattan Recall
     - type: manhattan_ap
-      value: 0.6033119142961784
+      value: 0.7072033306346501
       name: Manhattan Ap
     - type: euclidean_accuracy
-      value: 0.5387064978391084
+      value: 0.6590703290069424
       name: Euclidean Accuracy
     - type: euclidean_accuracy_threshold
-      value: 8.973075866699219
+      value: 12.141830444335938
       name: Euclidean Accuracy Threshold
     - type: euclidean_f1
-      value: 0.6834065495207667
+      value: 0.7036813518406759
       name: Euclidean F1
     - type: euclidean_f1_threshold
-      value: 24.51708221435547
+      value: 14.197540283203125
       name: Euclidean F1 Threshold
     - type: euclidean_precision
-      value: 0.5191505498672734
+      value: 0.5996708496194199
       name: Euclidean Precision
     - type: euclidean_recall
-      value: 0.9997079012706295
+      value: 0.8513434579439252
       name: Euclidean Recall
     - type: euclidean_ap
-      value: 0.577277049262529
+      value: 0.7035256676322055
       name: Euclidean Ap
     - type: max_accuracy
-      value: 0.5644855561452726
+      value: 0.6590703290069424
       name: Max Accuracy
     - type: max_accuracy_threshold
-      value: 391.4422302246094
+      value: 243.75453186035156
       name: Max Accuracy Threshold
     - type: max_f1
-      value: 0.6834381551362683
+      value: 0.7055783910745744
       name: Max F1
     - type: max_f1_threshold
-      value: 322.75946044921875
+      value: 295.95947265625
       name: Max F1 Threshold
     - type: max_precision
-      value: 0.5191505498672734
+      value: 0.6115046147241897
       name: Max Precision
     - type: max_recall
-      value: 0.9998539506353147
+      value: 0.914427570093458
       name: Max Recall
     - type: max_ap
-      value: 0.6033119142961784
+      value: 0.7072033306346501
       name: Max Ap
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: pearson_cosine
+      value: 0.732169941341086
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7344587206087978
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.7537099624360986
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.7550555196955944
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.7468210439584286
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.74849026008206
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.6142835401925993
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6100201108417316
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.7537099624360986
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.7550555196955944
+      name: Spearman Max
 ---
 
 # SentenceTransformer based on microsoft/deberta-v3-small
@@ -245,9 +292,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2")
 # Run inference
 sentences = [
-    'This may be overly obvious, but in American English, saying "you\'re welcome" is certainly polite and standard.',
-    'I\'m not sure how "Not at all" sounds in response to "thank you".',
-    "As bikeboy389 said, you can learn a lot by looking at students' native languages.",
+    'A wet child stands in chest deep ocean water.',
+    'The child s playing on the beach.',
+    'A woman paints a portrait of her best friend.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -293,41 +340,58 @@ You can finetune this model on your own dataset.
 
 | Metric                       | Value      |
 |:-----------------------------|:-----------|
-| cosine_accuracy              | 0.5398     |
-| cosine_accuracy_threshold    | 0.9089     |
-| cosine_f1                    | 0.6834     |
-| cosine_f1_threshold          | 0.3752     |
-| cosine_precision             | 0.5191     |
-| cosine_recall                | 0.9999     |
-| cosine_ap                    | 0.5795     |
-| dot_accuracy                 | 0.5303     |
-| dot_accuracy_threshold       | 391.4422   |
-| dot_f1                       | 0.6834     |
-| dot_f1_threshold             | 175.0789   |
-| dot_precision                | 0.5191     |
-| dot_recall                   | 0.9999     |
-| dot_ap                       | 0.5622     |
-| manhattan_accuracy           | 0.5645     |
-| manhattan_accuracy_threshold | 160.0457   |
-| manhattan_f1                 | 0.6834     |
-| manhattan_f1_threshold       | 322.7595   |
-| manhattan_precision          | 0.5191     |
-| manhattan_recall             | 0.9999     |
-| manhattan_ap                 | 0.6033     |
-| euclidean_accuracy           | 0.5387     |
-| euclidean_accuracy_threshold | 8.9731     |
-| euclidean_f1                 | 0.6834     |
-| euclidean_f1_threshold       | 24.5171    |
-| euclidean_precision          | 0.5192     |
-| euclidean_recall             | 0.9997     |
-| euclidean_ap                 | 0.5773     |
-| max_accuracy                 | 0.5645     |
-| max_accuracy_threshold       | 391.4422   |
-| max_f1                       | 0.6834     |
-| max_f1_threshold             | 322.7595   |
-| max_precision                | 0.5192     |
-| max_recall                   | 0.9999     |
-| **max_ap**                   | **0.6033** |
+| cosine_accuracy              | 0.6583     |
+| cosine_accuracy_threshold    | 0.6767     |
+| cosine_f1                    | 0.7049     |
+| cosine_f1_threshold          | 0.6018     |
+| cosine_precision             | 0.6115     |
+| cosine_recall                | 0.8321     |
+| cosine_ap                    | 0.6995     |
+| dot_accuracy                 | 0.6272     |
+| dot_accuracy_threshold       | 163.2505   |
+| dot_f1                       | 0.6976     |
+| dot_f1_threshold             | 119.2078   |
+| dot_precision                | 0.5639     |
+| dot_recall                   | 0.9144     |
+| dot_ap                       | 0.6437     |
+| manhattan_accuracy           | 0.6571     |
+| manhattan_accuracy_threshold | 243.7545   |
+| manhattan_f1                 | 0.7056     |
+| manhattan_f1_threshold       | 295.9595   |
+| manhattan_precision          | 0.5901     |
+| manhattan_recall             | 0.8773     |
+| manhattan_ap                 | 0.7072     |
+| euclidean_accuracy           | 0.6591     |
+| euclidean_accuracy_threshold | 12.1418    |
+| euclidean_f1                 | 0.7037     |
+| euclidean_f1_threshold       | 14.1975    |
+| euclidean_precision          | 0.5997     |
+| euclidean_recall             | 0.8513     |
+| euclidean_ap                 | 0.7035     |
+| max_accuracy                 | 0.6591     |
+| max_accuracy_threshold       | 243.7545   |
+| max_f1                       | 0.7056     |
+| max_f1_threshold             | 295.9595   |
+| max_precision                | 0.6115     |
+| max_recall                   | 0.9144     |
+| **max_ap**                   | **0.7072** |
+
+#### Semantic Similarity
+
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.7322     |
+| **spearman_cosine** | **0.7345** |
+| pearson_manhattan   | 0.7537     |
+| spearman_manhattan  | 0.7551     |
+| pearson_euclidean   | 0.7468     |
+| spearman_euclidean  | 0.7485     |
+| pearson_dot         | 0.6143     |
+| spearman_dot        | 0.61       |
+| pearson_max         | 0.7537     |
+| spearman_max        | 0.7551     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -348,19 +412,19 @@ You can finetune this model on your own dataset.
 #### stanfordnlp/snli
 
 * Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
-* Size: 314,315 training samples
+* Size: 67,190 training samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                         | sentence2                                                                        | label                        |
-  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
-  | type    | string                                                                            | string                                                                           | int                          |
-  | details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
+  |         | sentence1                                                                          | sentence2                                                                         | label                        |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                             | string                                                                            | int                          |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 21.19 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.77 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
 * Samples:
-  | sentence1                                                                  | sentence2                                        | label          |
-  |:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
-  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>0</code> |
-  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>0</code> |
-  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
+  | sentence1                                                                                                                              | sentence2                                                                                        | label          |
+  |:---------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Without a placebo group, we still won't know if any of the treatments are better than nothing and therefore worth giving.</code> | <code>It is necessary to use a controlled method to ensure the treatments are worthwhile.</code> | <code>0</code> |
+  | <code>It was conducted in silence.</code>                                                                                              | <code>It was done silently.</code>                                                               | <code>0</code> |
+  | <code>oh Lewisville  any decent food in your cafeteria up there</code>                                                                 | <code>Is there any decent food in your cafeteria up there in Lewisville?</code>                  | <code>0</code> |
 * Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
   ```json
   {
@@ -403,10 +467,162 @@ You can finetune this model on your own dataset.
   }
   ```
 
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 42
+- `per_device_eval_batch_size`: 22
+- `learning_rate`: 3e-06
+- `weight_decay`: 1e-08
+- `num_train_epochs`: 2
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.5
+- `save_safetensors`: False
+- `fp16`: True
+- `hub_model_id`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp
+- `hub_strategy`: checkpoint
+- `hub_private_repo`: True
+- `batch_sampler`: no_duplicates
+
+#### 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`: 42
+- `per_device_eval_batch_size`: 22
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 3e-06
+- `weight_decay`: 1e-08
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.5
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: False
+- `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`: False
+- `fp16`: True
+- `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`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp
+- `hub_strategy`: checkpoint
+- `hub_private_repo`: True
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
 ### Training Logs
-| Epoch | Step | loss   | max_ap |
-|:-----:|:----:|:------:|:------:|
-| None  | 0    | 4.6204 | 0.6033 |
+| Epoch | Step | Training Loss | loss   | max_ap | spearman_cosine |
+|:-----:|:----:|:-------------:|:------:|:------:|:---------------:|
+| 0.1   | 160  | 4.6003        | 4.8299 | 0.6017 | -               |
+| 0.2   | 320  | 4.0659        | 4.3436 | 0.6168 | -               |
+| 0.3   | 480  | 3.4886        | 4.0840 | 0.6339 | -               |
+| 0.4   | 640  | 3.0592        | 3.6422 | 0.6611 | -               |
+| 0.5   | 800  | 2.5728        | 3.1927 | 0.6773 | -               |
+| 0.6   | 960  | 2.184         | 2.8322 | 0.6893 | -               |
+| 0.7   | 1120 | 1.8744        | 2.4892 | 0.6954 | -               |
+| 0.8   | 1280 | 1.757         | 2.4453 | 0.7002 | -               |
+| 0.9   | 1440 | 1.5872        | 2.2565 | 0.7010 | -               |
+| 1.0   | 1600 | 1.446         | 2.1391 | 0.7046 | -               |
+| 1.1   | 1760 | 1.3892        | 2.1236 | 0.7058 | -               |
+| 1.2   | 1920 | 1.2567        | 1.9738 | 0.7053 | -               |
+| 1.3   | 2080 | 1.2233        | 1.8925 | 0.7063 | -               |
+| 1.4   | 2240 | 1.1954        | 1.8392 | 0.7075 | -               |
+| 1.5   | 2400 | 1.1395        | 1.9081 | 0.7065 | -               |
+| 1.6   | 2560 | 1.1211        | 1.8080 | 0.7074 | -               |
+| 1.7   | 2720 | 1.0825        | 1.8408 | 0.7073 | -               |
+| 1.8   | 2880 | 1.1358        | 1.7363 | 0.7073 | -               |
+| 1.9   | 3040 | 1.0628        | 1.8936 | 0.7072 | -               |
+| 2.0   | 3200 | 1.1412        | 1.7846 | 0.7072 | -               |
+| None  | 0    | -             | 3.0121 | 0.7072 | 0.7345          |
 
 
 ### Framework Versions

pytorch_model.bin

@@ -1,3 +1,3 @@
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 size 565251810