Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: يا حاقد ع الاسلام السياسي
+- text: 'بلد مخيف، صار القتل بحجه الشرف متل قتل بعوضة، واللي بيخوف اكتر من اللي واقف
+    مكتف ايديه ومش مساعد. وين كنآ، ووين وصلنآ، لمتى حنضل عايشين وساكتين!
+
+    '
+- text: "من خلال المتابعة ..يتضح أن أكثر اللاعبين الذين يتم تسويقهم هم لاعبي امريكا\
+    \ الجنوبية وأقلهم الافارقة.  \nمن خلال الواقع ..أكثر اللاعبين تهاونا ولعب على\
+    \ الواقف  في آخر ٦ شهور من عقودهم  هم لاعبي امريكا الجنوبية ."
+- text: ' علم الحزب يا فهمانه ما حطوا لانه عم يحكي وطنيا ومشان ماحدا متلك يعترض. اذا
+    حطوا بتعترضي واذا ما حطوا كمان بتعترضي.'
+- text: "شيوعي \nعلماني \nمسيحي\nانصار سنه \nصوفي \nيمثلك التجمع \nلا يمثلك التجمع\
+    \ \nاهلا بكم جميعا فنحن نريد بناء وطن ❤"
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: akhooli/sbert-nli-500k-triplets-MB
+model-index:
+- name: SetFit with akhooli/sbert-nli-500k-triplets-MB
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7956709956709956
+      name: Accuracy
+---
+
+# SetFit with akhooli/sbert-nli-500k-triplets-MB
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert-nli-500k-triplets-MB](https://huggingface.co/akhooli/sbert-nli-500k-triplets-MB) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [akhooli/sbert-nli-500k-triplets-MB](https://huggingface.co/akhooli/sbert-nli-500k-triplets-MB)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label    | Examples                                                                                                                                                                                                                                     |
+|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| positive | <ul><li>' سبحان الله الفلسطينيين شعب خاين في كل مكان \nلاحول ولا قوة إلا بالله'</li><li>'يا بيك عّم تخبرنا عن شي ما فينا تعملو نحن ماًعندنا نواب ولا وزراء بمثلونا بالدولة الا اذا زهقان وعبالك ليك'</li><li>'جوز كذابين منافقين…'</li></ul> |
+| negative | <ul><li>'ربي لا تجعلني أسيء الظن بأحد ولا تجعل في قلبي شيئا على أحد ، اللهم أسألك قلباً نقياً صافيا'</li><li>'هشام حداد عامل فيها جون ستيوارت'</li><li>'     بحياة اختك من وين بتجيبي اخبارك؟؟ من صغري وانا عبالي كون… LINK'</li></ul>       |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7957   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("akhooli/setfit_ar_hs_mb")
+# Run inference
+preds = model("يا حاقد ع الاسلام السياسي")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## 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 Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 1   | 18.8388 | 185 |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 5200                  |
+| positive | 4943                  |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: 6000
+- sampling_strategy: undersampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- run_name: setfit_hate_52k_mb_6k
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.3373        | -               |
+| 0.0333 | 100  | 0.2955        | -               |
+| 0.0667 | 200  | 0.2535        | -               |
+| 0.1    | 300  | 0.2373        | -               |
+| 0.1333 | 400  | 0.2228        | -               |
+| 0.1667 | 500  | 0.1956        | -               |
+| 0.2    | 600  | 0.1768        | -               |
+| 0.2333 | 700  | 0.1489        | -               |
+| 0.2667 | 800  | 0.122         | -               |
+| 0.3    | 900  | 0.1045        | -               |
+| 0.3333 | 1000 | 0.086         | -               |
+| 0.3667 | 1100 | 0.0681        | -               |
+| 0.4    | 1200 | 0.067         | -               |
+| 0.4333 | 1300 | 0.0477        | -               |
+| 0.4667 | 1400 | 0.043         | -               |
+| 0.5    | 1500 | 0.0316        | -               |
+| 0.5333 | 1600 | 0.0251        | -               |
+| 0.5667 | 1700 | 0.0236        | -               |
+| 0.6    | 1800 | 0.0163        | -               |
+| 0.6333 | 1900 | 0.0148        | -               |
+| 0.6667 | 2000 | 0.0105        | -               |
+| 0.7    | 2100 | 0.018         | -               |
+| 0.7333 | 2200 | 0.013         | -               |
+| 0.7667 | 2300 | 0.0103        | -               |
+| 0.8    | 2400 | 0.0107        | -               |
+| 0.8333 | 2500 | 0.0115        | -               |
+| 0.8667 | 2600 | 0.0069        | -               |
+| 0.9    | 2700 | 0.0062        | -               |
+| 0.9333 | 2800 | 0.0074        | -               |
+| 0.9667 | 2900 | 0.0063        | -               |
+| 1.0    | 3000 | 0.0068        | -               |
+| 1.0333 | 3100 | 0.0048        | -               |
+| 1.0667 | 3200 | 0.0055        | -               |
+| 1.1    | 3300 | 0.0047        | -               |
+| 1.1333 | 3400 | 0.0043        | -               |
+| 1.1667 | 3500 | 0.0029        | -               |
+| 1.2    | 3600 | 0.0036        | -               |
+| 1.2333 | 3700 | 0.0034        | -               |
+| 1.2667 | 3800 | 0.0024        | -               |
+| 1.3    | 3900 | 0.0033        | -               |
+| 1.3333 | 4000 | 0.0042        | -               |
+| 1.3667 | 4100 | 0.0039        | -               |
+| 1.4    | 4200 | 0.0019        | -               |
+| 1.4333 | 4300 | 0.0022        | -               |
+| 1.4667 | 4400 | 0.0031        | -               |
+| 1.5    | 4500 | 0.0019        | -               |
+| 1.5333 | 4600 | 0.0036        | -               |
+| 1.5667 | 4700 | 0.0017        | -               |
+| 1.6    | 4800 | 0.0007        | -               |
+| 1.6333 | 4900 | 0.0006        | -               |
+| 1.6667 | 5000 | 0.0019        | -               |
+| 1.7    | 5100 | 0.0022        | -               |
+| 1.7333 | 5200 | 0.0013        | -               |
+| 1.7667 | 5300 | 0.0025        | -               |
+| 1.8    | 5400 | 0.0024        | -               |
+| 1.8333 | 5500 | 0.0013        | -               |
+| 1.8667 | 5600 | 0.0022        | -               |
+| 1.9    | 5700 | 0.0022        | -               |
+| 1.9333 | 5800 | 0.0019        | -               |
+| 1.9667 | 5900 | 0.0019        | -               |
+| 2.0    | 6000 | 0.0031        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.2.0.dev0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.48.0
+- PyTorch: 2.5.1+cu121
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

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+{
+  "_name_or_path": "akhooli/sbert-nli-500k-triplets-MB",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 3,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": null,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 10000,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 5,
+  "reference_compile": false,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 4,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.0",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "negative",
+    "positive"
+  ]
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
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+}

tokenizer_config.json

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+}