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.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": 384,
+  "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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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: intfloat/multilingual-e5-small
+metrics:
+- accuracy
+widget:
+- text: 'query: Sí, la próxima vez que vayas, cuenta conmigo. He querido salir y hacer
+    más actividades en la naturaleza.'
+- text: 'query: I''m man, I''m leaving now.'
+- text: 'query: Ja, forse possiamo fare un giro in bicicletta insieme.'
+- text: 'query: Mak saya suruh balik, jumpa lagi.'
+- text: 'query: İnanılmaz, bu harika! Bir ayı gördüğüne inanamıyorum!'
+pipeline_tag: text-classification
+inference: true
+---
+
+# SetFit with intfloat/multilingual-e5-small
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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-small](https://huggingface.co/intfloat/multilingual-e5-small)
+- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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                                                                                                                                                                                                               |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'query: Tja, måste dra nu, ses senare.'</li><li>'query: Ispričavam se, moram sada otići.'</li><li>'query: Przepraszam, muszę już iść.'</li></ul>                                                               |
+| 0     | <ul><li>'query: Sveiki, kā jums klājas?'</li><li>'query: அதிர்ச்சிகரமானது, அது மிகவும் அருமையாக இருக்கிறது! நீ கரடியை பார்த்தது எனக்கு நம்பிக்கையே வரவில்லை!'</li><li>'query: Ég hef það fínt, takk. Og þú?'</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("setfit_model_id")
+# Run inference
+preds = model("query: I'm man, I'm leaving now.")
+```
+
+<!--
+### 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   | 2   | 7.6965 | 31  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 902                   |
+| 1     | 910                   |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 16)
+- max_steps: -1
+- sampling_strategy: undersampling
+- body_learning_rate: (1e-05, 1e-05)
+- head_learning_rate: 0.001
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.1
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- run_name: multilingual-e5-small
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0000 | 1    | 0.3613        | -               |
+| 0.0005 | 50   | 0.3577        | -               |
+| 0.0010 | 100  | 0.3511        | 0.3413          |
+| 0.0015 | 150  | 0.3372        | -               |
+| 0.0019 | 200  | 0.3447        | 0.3347          |
+| 0.0024 | 250  | 0.3349        | -               |
+| 0.0029 | 300  | 0.3326        | 0.3224          |
+| 0.0034 | 350  | 0.3372        | -               |
+| 0.0039 | 400  | 0.3185        | 0.3039          |
+| 0.0044 | 450  | 0.2828        | -               |
+| 0.0049 | 500  | 0.3055        | 0.2774          |
+| 0.0054 | 550  | 0.2594        | -               |
+| 0.0058 | 600  | 0.2779        | 0.2489          |
+| 0.0063 | 650  | 0.2486        | -               |
+| 0.0068 | 700  | 0.2321        | 0.22            |
+| 0.0073 | 750  | 0.1838        | -               |
+| 0.0078 | 800  | 0.1845        | 0.2075          |
+| 0.0083 | 850  | 0.1899        | -               |
+| 0.0088 | 900  | 0.2147        | 0.2025          |
+| 0.0093 | 950  | 0.1644        | -               |
+| 0.0097 | 1000 | 0.2019        | 0.1821          |
+| 0.0102 | 1050 | 0.2309        | -               |
+| 0.0107 | 1100 | 0.2084        | 0.1784          |
+| 0.0112 | 1150 | 0.1508        | -               |
+| 0.0117 | 1200 | 0.1064        | 0.1453          |
+| 0.0122 | 1250 | 0.1376        | -               |
+| 0.0127 | 1300 | 0.0828        | 0.121           |
+| 0.0132 | 1350 | 0.1628        | -               |
+| 0.0136 | 1400 | 0.1308        | 0.1018          |
+| 0.0141 | 1450 | 0.0566        | -               |
+| 0.0146 | 1500 | 0.0953        | 0.0767          |
+| 0.0151 | 1550 | 0.1607        | -               |
+| 0.0156 | 1600 | 0.1322        | 0.0625          |
+| 0.0161 | 1650 | 0.0861        | -               |
+| 0.0166 | 1700 | 0.0926        | 0.0423          |
+| 0.0171 | 1750 | 0.0338        | -               |
+| 0.0175 | 1800 | 0.1029        | 0.0344          |
+| 0.0180 | 1850 | 0.0442        | -               |
+| 0.0185 | 1900 | 0.019         | 0.0256          |
+| 0.0190 | 1950 | 0.0489        | -               |
+| 0.0195 | 2000 | 0.0675        | 0.0187          |
+
+### Framework Versions
+- Python: 3.10.11
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.39.0
+- PyTorch: 2.4.0
+- Datasets: 2.20.0
+- Tokenizers: 0.15.2
+
+## 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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+{
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+  "architectures": [
+    "BertModel"
+  ],
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+  "classifier_dropout": null,
+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-12,
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+  "model_type": "bert",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "2.7.0",
+    "transformers": "4.39.0",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+  "labels": null,
+  "normalize_embeddings": false
+}

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modules.json

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+  }
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sentence_bert_config.json

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special_tokens_map.json

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