Update readme.md

README.md

@@ -2,6 +2,7 @@
 base_model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
 datasets:
 - Omartificial-Intelligence-Space/Arabic-stsb
+- Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class
 language:
 - ar
 library_name: sentence-transformers
@@ -16,6 +17,7 @@ metrics:
 - spearman_dot
 - pearson_max
 - spearman_max
+- mteb
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
@@ -25,6 +27,7 @@ tags:
 - dataset_size:947818
 - loss:SoftmaxLoss
 - loss:CosineSimilarityLoss
+- transformers
 widget:
 - source_sentence: امرأة تكتب شيئاً
   sentences:
@@ -36,14 +39,14 @@ widget:
   - لاعب كرة السلة على وشك تسجيل نقاط لفريقه.
   - المقال التالي مأخوذ من نسختي من "أطلس البطريق الجديد للتاريخ الوسطى"
   - قد يكون من الممكن أن يوجد نظام شمسي مثل نظامنا خارج المجرة
-- source_sentence: تحت السماء الزرقاء مع الغيوم البيضاء، يصل طفل لمس مروحة طائرة واقفة
-    على حقل من العشب.
+- source_sentence: >-
+    تحت السماء الزرقاء مع الغيوم البيضاء، يصل طفل لمس مروحة طائرة واقفة على حقل
+    من العشب.
   sentences:
   - امرأة تحمل كأساً
   - طفل يحاول لمس مروحة طائرة
   - اثنان من عازبين عن الشرب يستعدون للعشاء
-- source_sentence: رجل في منتصف العمر يحلق لحيته في غرفة ذات جدران بيضاء والتي لا
-    تبدو كحمام
+- source_sentence: رجل في منتصف العمر يحلق لحيته في غرفة ذات جدران بيضاء والتي لا تبدو كحمام
   sentences:
   - فتى يخطط اسمه على مكتبه
   - رجل ينام
@@ -54,7 +57,9 @@ widget:
   - المرأة تنظر من النافذة.
   - لقد مات الكلب
 model-index:
-- name: SentenceTransformer based on Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
+- name: >-
+    SentenceTransformer based on
+    Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
   results:
   - task:
       type: semantic-similarity
@@ -130,11 +135,12 @@ model-index:
     - type: spearman_max
       value: 0.8172511596569861
       name: Spearman Max
+license: apache-2.0
 ---
 
-# SentenceTransformer based on Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2
+# GATE-AraBert-v1
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2](https://huggingface.co/Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2) on the all-nli and [sts](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a General Arabic Text Embedding trained using SentenceTransformers in a multi-task setup. The system trains on the AllNLI and on the STS dataset.
 
 ## Model Details
 
@@ -145,25 +151,10 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [O
 - **Output Dimensionality:** 768 tokens
 - **Similarity Function:** Cosine Similarity
 - **Training Datasets:**
-    - all-nli
+    - [all-nli](https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Pair-Class)
     - [sts](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb)
 - **Language:** ar
-<!-- - **License:** Unknown -->
 
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
 
 ## Usage
 
@@ -180,7 +171,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2-multi-tas-v2k")
+model = SentenceTransformer("Omartificial-Intelligence-Space/GATE-AraBert-v1")
 # Run inference
 sentences = [
     'الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.',
@@ -197,29 +188,6 @@ print(similarities.shape)
 # [3, 3]
 ```
 
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
 
 ## Evaluation
 
@@ -259,253 +227,6 @@ You can finetune this model on your own dataset.
 | pearson_max         | 0.813      |
 | spearman_max        | 0.8173     |
 
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Datasets
-
-#### all-nli
-
-* Dataset: all-nli
-* Size: 942,069 training samples
-* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | premise                                                                           | hypothesis                                                                       | label                                                              |
-  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                           | int                                                                |
-  | details | <ul><li>min: 5 tokens</li><li>mean: 14.09 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.28 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
-* Samples:
-  | premise                                        | hypothesis                                  | label          |
-  |:-----------------------------------------------|:--------------------------------------------|:---------------|
-  | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص يقوم بتدريب حصانه للمنافسة</code> | <code>1</code> |
-  | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في مطعم، يطلب عجة.</code>         | <code>2</code> |
-  | <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>0</code> |
-* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
-
-#### sts
-
-* Dataset: [sts](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb) at [f5a6f89](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb/tree/f5a6f89da460d307eff3acbbfcb62d0705cdbbb5)
-* Size: 5,749 training samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
-  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                           | float                                                          |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 7.46 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.36 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence1                                      | sentence2                                               | score             |
-  |:-----------------------------------------------|:--------------------------------------------------------|:------------------|
-  | <code>طائرة ستقلع</code>                       | <code>طائرة جوية ستقلع</code>                           | <code>1.0</code>  |
-  | <code>رجل يعزف على ناي كبير</code>             | <code>رجل يعزف على الناي.</code>                        | <code>0.76</code> |
-  | <code>رجل ينشر الجبن الممزق على البيتزا</code> | <code>رجل ينشر الجبن الممزق على بيتزا غير مطبوخة</code> | <code>0.76</code> |
-* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
-  ```json
-  {
-      "loss_fct": "torch.nn.modules.loss.MSELoss"
-  }
-  ```
-
-### Evaluation Datasets
-
-#### all-nli
-
-* Dataset: all-nli
-* Size: 1,000 evaluation samples
-* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | premise                                                                          | hypothesis                                                                       | label                                                              |
-  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
-  | type    | string                                                                           | string                                                                           | int                                                                |
-  | details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.11 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
-* Samples:
-  | premise                                         | hypothesis                                                                    | label          |
-  |:------------------------------------------------|:------------------------------------------------------------------------------|:---------------|
-  | <code>امرأتان يتعانقان بينما يحملان طرود</code> | <code>الأخوات يعانقون بعضهم لوداعاً بينما يحملون حزمة بعد تناول الغداء</code> | <code>1</code> |
-  | <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code>                                              | <code>0</code> |
-  | <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code>                                        | <code>2</code> |
-* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
-
-#### sts
-
-* Dataset: [sts](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb) at [f5a6f89](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb/tree/f5a6f89da460d307eff3acbbfcb62d0705cdbbb5)
-* Size: 1,500 evaluation samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            | float                                                          |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 12.55 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.49 tokens</li><li>max: 54 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>رجل يرتدي قبعة صلبة يرقص</code> | <code>رجل يرتدي قبعة صلبة يرقص.</code> | <code>1.0</code>  |
-  | <code>طفل صغير يركب حصاناً.</code>    | <code>طفل يركب حصاناً.</code>          | <code>0.95</code> |
-  | <code>رجل يطعم فأراً لأفعى</code>     | <code>الرجل يطعم الفأر للثعبان.</code> | <code>1.0</code>  |
-* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
-  ```json
-  {
-      "loss_fct": "torch.nn.modules.loss.MSELoss"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 16
-- `per_device_eval_batch_size`: 16
-- `num_train_epochs`: 1
-- `warmup_ratio`: 0.1
-- `fp16`: True
-- `multi_dataset_batch_sampler`: round_robin
-
-#### 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`: 16
-- `per_device_eval_batch_size`: 16
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 1
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.1
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `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`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: False
-- `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
-- `eval_on_start`: False
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss | sts loss | all-nli loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
-|:------:|:----:|:-------------:|:--------:|:------------:|:-----------------------:|:------------------------:|
-| 0.1389 | 100  | 0.5684        | 0.0279   | 1.0702       | 0.8484                  | -                        |
-| 0.2778 | 200  | 0.5085        | 0.0289   | 0.9511       | 0.8446                  | -                        |
-| 0.4167 | 300  | 0.4974        | 0.0283   | 0.9229       | 0.8430                  | -                        |
-| 0.5556 | 400  | 0.4672        | 0.0293   | 0.9221       | 0.8378                  | -                        |
-| 0.6944 | 500  | 0.4889        | 0.0300   | 0.8995       | 0.8360                  | -                        |
-| 0.8333 | 600  | 0.4711        | 0.0303   | 0.8683       | 0.8330                  | -                        |
-| 0.9722 | 700  | 0.4497        | 0.0291   | 0.8657       | 0.8410                  | -                        |
-| 1.0    | 720  | -             | -        | -            | -                       | 0.8173                   |
-
-
-### Framework Versions
-- Python: 3.9.18
-- Sentence Transformers: 3.0.1
-- Transformers: 4.42.4
-- PyTorch: 2.2.2+cu121
-- Accelerate: 0.26.1
-- Datasets: 2.19.0
-- Tokenizers: 0.19.1
 
 ## Citation
 
@@ -524,20 +245,3 @@ You can finetune this model on your own dataset.
 }
 ```
 
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->