Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "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

@@ -0,0 +1,263 @@
+---
+base_model: intfloat/multilingual-e5-large-instruct
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '"Он подарил мне красивое кольцо и прекрасную вечеринку на нашу годовщину."
+    Бұл мәтінді қазақ тіліне аударып беріңізші.'
+- text: Would you please put that cigarette out? I get sick on it.
+- text: Сәлем!
+- text: Никусор Эшану
+- text: How time flies! We have been lovers for nearly a year. We hit it off instantly.
+inference: true
+model-index:
+- name: SetFit with intfloat/multilingual-e5-large-instruct
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9955398215928637
+      name: Accuracy
+---
+
+# SetFit with intfloat/multilingual-e5-large-instruct
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) 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:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 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                                                                                                                                                                                          |
+|:-------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| rag    | <ul><li>'Саксон эпизоды туралы қандай тарихи құжатта мәлімет берілген?'</li><li>'Uttermost өзінің жарыс мансабында қандай маңызды жетістіктерге қол жеткізді?'</li><li>'Ричард Бахтелл'</li></ul> |
+| no_rag | <ul><li>'Just a moment, please.'</li><li>'орыс тіліндегі "Я рабочий." сөйлемінің қазақ тіліндегі аудармасы не?'</li><li>'You look tired. Did you sleep well last night?'</li></ul>                |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9955   |
+
+## 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("nlp-team-issai/setfit-me5-large-instruct-v3")
+# Run inference
+preds = model("Сәлем!")
+```
+
+<!--
+### 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   | 10.0022 | 138 |
+
+| Label  | Training Sample Count |
+|:-------|:----------------------|
+| no_rag | 218                   |
+| rag    | 241                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- 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
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0003 | 1    | 0.3567        | -               |
+| 0.0151 | 50   | 0.2851        | -               |
+| 0.0302 | 100  | 0.0943        | -               |
+| 0.0452 | 150  | 0.0123        | -               |
+| 0.0603 | 200  | 0.0099        | -               |
+| 0.0754 | 250  | 0.0056        | -               |
+| 0.0905 | 300  | 0.0011        | -               |
+| 0.1056 | 350  | 0.0003        | -               |
+| 0.1207 | 400  | 0.0002        | -               |
+| 0.1357 | 450  | 0.0001        | -               |
+| 0.1508 | 500  | 0.0001        | -               |
+| 0.1659 | 550  | 0.0001        | -               |
+| 0.1810 | 600  | 0.0001        | -               |
+| 0.1961 | 650  | 0.0001        | -               |
+| 0.2112 | 700  | 0.0001        | -               |
+| 0.2262 | 750  | 0.0001        | -               |
+| 0.2413 | 800  | 0.0001        | -               |
+| 0.2564 | 850  | 0.0001        | -               |
+| 0.2715 | 900  | 0.0001        | -               |
+| 0.2866 | 950  | 0.0001        | -               |
+| 0.3017 | 1000 | 0.0001        | -               |
+| 0.3167 | 1050 | 0.0001        | -               |
+| 0.3318 | 1100 | 0.0001        | -               |
+| 0.3469 | 1150 | 0.0001        | -               |
+| 0.3620 | 1200 | 0.0001        | -               |
+| 0.3771 | 1250 | 0.0001        | -               |
+| 0.3922 | 1300 | 0.0001        | -               |
+| 0.4072 | 1350 | 0.0001        | -               |
+| 0.4223 | 1400 | 0.0           | -               |
+| 0.4374 | 1450 | 0.0           | -               |
+| 0.4525 | 1500 | 0.0           | -               |
+| 0.4676 | 1550 | 0.0           | -               |
+| 0.4827 | 1600 | 0.0           | -               |
+| 0.4977 | 1650 | 0.0           | -               |
+| 0.5128 | 1700 | 0.0           | -               |
+| 0.5279 | 1750 | 0.0           | -               |
+| 0.5430 | 1800 | 0.0           | -               |
+| 0.5581 | 1850 | 0.0           | -               |
+| 0.5732 | 1900 | 0.0           | -               |
+| 0.5882 | 1950 | 0.0           | -               |
+| 0.6033 | 2000 | 0.0           | -               |
+| 0.6184 | 2050 | 0.0           | -               |
+| 0.6335 | 2100 | 0.0           | -               |
+| 0.6486 | 2150 | 0.0           | -               |
+| 0.6637 | 2200 | 0.0           | -               |
+| 0.6787 | 2250 | 0.0           | -               |
+| 0.6938 | 2300 | 0.0           | -               |
+| 0.7089 | 2350 | 0.0           | -               |
+| 0.7240 | 2400 | 0.0           | -               |
+| 0.7391 | 2450 | 0.0           | -               |
+| 0.7541 | 2500 | 0.0           | -               |
+| 0.7692 | 2550 | 0.0           | -               |
+| 0.7843 | 2600 | 0.0           | -               |
+| 0.7994 | 2650 | 0.0           | -               |
+| 0.8145 | 2700 | 0.0           | -               |
+| 0.8296 | 2750 | 0.0           | -               |
+| 0.8446 | 2800 | 0.0           | -               |
+| 0.8597 | 2850 | 0.0           | -               |
+| 0.8748 | 2900 | 0.0           | -               |
+| 0.8899 | 2950 | 0.0           | -               |
+| 0.9050 | 3000 | 0.0           | -               |
+| 0.9201 | 3050 | 0.0           | -               |
+| 0.9351 | 3100 | 0.0           | -               |
+| 0.9502 | 3150 | 0.0           | -               |
+| 0.9653 | 3200 | 0.0           | -               |
+| 0.9804 | 3250 | 0.0           | -               |
+| 0.9955 | 3300 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.12.5
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.0
+- Transformers: 4.45.2
+- PyTorch: 2.4.0+cu121
+- Datasets: 3.0.1
+- Tokenizers: 0.20.0
+
+## 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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+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "3.2.0",
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+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
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+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "no_rag",
+    "rag"
+  ]
+}

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sentence_bert_config.json

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