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 +1,233 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Also you guys are deducting data so much without using it seems
+- text: ඇත්තටම ගහෝල්ඩ් වටිනවා මම 285 පැකේජය පාවිච්චි කරනවා good
+- text: Gampola Lebsack chater
+- text: Unlimited A2A calls සමඟ අඩුම ගානට වැඩිම data දෙන පට්ට pack එක smiling_face_with_sunglasses
+- text: he shows hw experienced n great he is.. dats da whole turning poi t of da
+    match..
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: intfloat/multilingual-e5-large
+---
+
+# SetFit with intfloat/multilingual-e5-large
+
+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](https://huggingface.co/intfloat/multilingual-e5-large) 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](https://huggingface.co/intfloat/multilingual-e5-large)
+- **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:** 3 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                                                                                                                                                                                                                                                                                               |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Fiber nae.'</li><li>'hey Zemlak boru bill danne ai ewa refund karana kiwata reply na anthima horu tikakne'</li><li>'hey Zemlak boru bill danne ai ewa refund karana kiwata reply na horu tikakne'</li></ul>                                                                                   |
+| 2     | <ul><li>'I think this is the first time Pubg players are awarded with this much of a cash pool in SL.'</li><li>'I think this is the first time players are with this of a pool in'</li><li>'I think this is the first time Pubg players are awarded with in this much of a cash pool in SL.'</li></ul> |
+| 1     | <ul><li>'Koo ampara district alamkulam area 2g coverage'</li><li>'Koo ampara district area 2g coverage'</li><li>'Koo ampara district district alamkulam area 2g coverage'</li></ul>                                                                                                                    |
+
+## 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("vinulacs/sinmix-setfit-sentiment")
+# Run inference
+preds = model("Gampola Lebsack chater")
+```
+
+<!--
+### 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   | 9.7390 | 79  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 194                   |
+| 1     | 185                   |
+| 2     | 165                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 40
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.2675        | -               |
+| 0.0184 | 50   | 0.3054        | -               |
+| 0.0368 | 100  | 0.219         | -               |
+| 0.0551 | 150  | 0.1664        | -               |
+| 0.0735 | 200  | 0.0785        | -               |
+| 0.0919 | 250  | 0.0256        | -               |
+| 0.1103 | 300  | 0.009         | -               |
+| 0.1287 | 350  | 0.0104        | -               |
+| 0.1471 | 400  | 0.0004        | -               |
+| 0.1654 | 450  | 0.0003        | -               |
+| 0.1838 | 500  | 0.0003        | -               |
+| 0.2022 | 550  | 0.0002        | -               |
+| 0.2206 | 600  | 0.0002        | -               |
+| 0.2390 | 650  | 0.0002        | -               |
+| 0.2574 | 700  | 0.0002        | -               |
+| 0.2757 | 750  | 0.0002        | -               |
+| 0.2941 | 800  | 0.0001        | -               |
+| 0.3125 | 850  | 0.0001        | -               |
+| 0.3309 | 900  | 0.0001        | -               |
+| 0.3493 | 950  | 0.0001        | -               |
+| 0.3676 | 1000 | 0.0001        | -               |
+| 0.3860 | 1050 | 0.0001        | -               |
+| 0.4044 | 1100 | 0.0001        | -               |
+| 0.4228 | 1150 | 0.0001        | -               |
+| 0.4412 | 1200 | 0.0001        | -               |
+| 0.4596 | 1250 | 0.0001        | -               |
+| 0.4779 | 1300 | 0.0001        | -               |
+| 0.4963 | 1350 | 0.0001        | -               |
+| 0.5147 | 1400 | 0.0001        | -               |
+| 0.5331 | 1450 | 0.0001        | -               |
+| 0.5515 | 1500 | 0.0001        | -               |
+| 0.5699 | 1550 | 0.0001        | -               |
+| 0.5882 | 1600 | 0.0001        | -               |
+| 0.6066 | 1650 | 0.0001        | -               |
+| 0.625  | 1700 | 0.0001        | -               |
+| 0.6434 | 1750 | 0.0001        | -               |
+| 0.6618 | 1800 | 0.0001        | -               |
+| 0.6801 | 1850 | 0.0001        | -               |
+| 0.6985 | 1900 | 0.0001        | -               |
+| 0.7169 | 1950 | 0.0001        | -               |
+| 0.7353 | 2000 | 0.0001        | -               |
+| 0.7537 | 2050 | 0.0001        | -               |
+| 0.7721 | 2100 | 0.0001        | -               |
+| 0.7904 | 2150 | 0.0001        | -               |
+| 0.8088 | 2200 | 0.0001        | -               |
+| 0.8272 | 2250 | 0.0001        | -               |
+| 0.8456 | 2300 | 0.0001        | -               |
+| 0.8640 | 2350 | 0.0001        | -               |
+| 0.8824 | 2400 | 0.0001        | -               |
+| 0.9007 | 2450 | 0.0001        | -               |
+| 0.9191 | 2500 | 0.0001        | -               |
+| 0.9375 | 2550 | 0.0013        | -               |
+| 0.9559 | 2600 | 0.0001        | -               |
+| 0.9743 | 2650 | 0.0001        | -               |
+| 0.9926 | 2700 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.12.12
+- SetFit: 1.1.3
+- Sentence Transformers: 5.2.3
+- Transformers: 4.51.3
+- PyTorch: 2.10.0+cu128
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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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+    "XLMRobertaModel"
+  ],
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
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+  "use_cache": true,
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+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "4.51.3",
+    "pytorch": "2.10.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+  "labels": [
+    0,
+    1,
+    2
+  ],
+  "normalize_embeddings": false
+}

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modules.json

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sentence_bert_config.json

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