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": 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

@@ -0,0 +1,218 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Die elektronische Patientenakte ist ein großer Schritt nach vorn für das deutsche
+    Gesundheitswesen. Sie ermöglicht eine bessere Koordination zwischen Ärzten und
+    sorgt dafür, dass Patienten immer die richtigen Informationen erhalten.
+- text: Die ePA könnte das Gesundheitssystem verbessern, aber es ist noch unklar,
+    wie sie in der Praxis funktioniert.
+- text: Die Möglichkeit, meine Daten selbst zu verwalten und zu entscheiden, wer darauf
+    zugreifen kann, macht die ePA für mich sehr attraktiv.
+- text: Die ePA ist ein komplexes Thema, bei dem ich noch nicht weiß, ob ich dafür
+    oder dagegen bin.
+- text: Die ePA wird uns als Fortschritt verkauft, aber in Wirklichkeit eröffnet sie
+    nur neue Möglichkeiten für Missbrauch und Datenlecks.
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 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                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+|:-------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| neutral      | <ul><li>'Ich bin weder komplett für noch gegen die ePA. Es kommt darauf an, wie sie umgesetzt wird.'</li><li>'Die ePA wird von manchen begrüßt und von anderen kritisiert. Ich warte ab, wie sie sich entwickelt.'</li><li>'Ich bin neutral, was die ePA betrifft. Sie könnte sowohl Vorteile als auch Nachteile haben.'</li></ul>                                                                                                        |
+| ablehnend    | <ul><li>'Ich habe kein Vertrauen in die ePA, weil es zu viele ungelöste Probleme gibt, besonders was den Datenschutz betrifft.'</li><li>'Ich lehne die ePA ab, weil ich nicht möchte, dass so viele Informationen über mich zentral gespeichert werden.'</li><li>'Die ePA ist ein weiterer Schritt in Richtung Überwachung. Ich habe Bedenken, dass Versicherungen und Arbeitgeber irgendwann Zugriff darauf erhalten könnten.'</li></ul> |
+| befürwortend | <ul><li>'Ich finde es gut, dass mit der ePA auch Notfalldaten sofort verfügbar sind, das kann im Ernstfall Leben retten.'</li><li>'Die ePA ist ein wichtiger Schritt in Richtung Digitalisierung und Modernisierung des Gesundheitssystems.'</li><li>'Ich sehe in der ePA eine große Chance, um den Austausch von Gesundheitsdaten zu verbessern und so die Behandlungsqualität zu steigern.'</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("Die ePA ist ein komplexes Thema, bei dem ich noch nicht weiß, ob ich dafür oder dagegen bin.")
+```
+
+<!--
+### 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   | 11  | 16.9365 | 23  |
+
+| Label        | Training Sample Count |
+|:-------------|:----------------------|
+| ablehnend    | 21                    |
+| neutral      | 21                    |
+| befürwortend | 21                    |
+
+### Training Hyperparameters
+- batch_size: (3, 3)
+- num_epochs: (2, 2)
+- 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: 63
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0011 | 1    | 0.151         | -               |
+| 0.0567 | 50   | 0.184         | -               |
+| 0.1134 | 100  | 0.1252        | -               |
+| 0.1701 | 150  | 0.0585        | -               |
+| 0.2268 | 200  | 0.0116        | -               |
+| 0.2834 | 250  | 0.0039        | -               |
+| 0.3401 | 300  | 0.002         | -               |
+| 0.3968 | 350  | 0.0013        | -               |
+| 0.4535 | 400  | 0.0007        | -               |
+| 0.5102 | 450  | 0.0008        | -               |
+| 0.5669 | 500  | 0.0005        | -               |
+| 0.6236 | 550  | 0.0005        | -               |
+| 0.6803 | 600  | 0.0004        | -               |
+| 0.7370 | 650  | 0.0004        | -               |
+| 0.7937 | 700  | 0.0003        | -               |
+| 0.8503 | 750  | 0.0003        | -               |
+| 0.9070 | 800  | 0.0003        | -               |
+| 0.9637 | 850  | 0.0002        | -               |
+| 1.0204 | 900  | 0.0002        | -               |
+| 1.0771 | 950  | 0.0001        | -               |
+| 1.1338 | 1000 | 0.0002        | -               |
+| 1.1905 | 1050 | 0.0001        | -               |
+| 1.2472 | 1100 | 0.0002        | -               |
+| 1.3039 | 1150 | 0.0002        | -               |
+| 1.3605 | 1200 | 0.0002        | -               |
+| 1.4172 | 1250 | 0.0001        | -               |
+| 1.4739 | 1300 | 0.0001        | -               |
+| 1.5306 | 1350 | 0.0001        | -               |
+| 1.5873 | 1400 | 0.0001        | -               |
+| 1.6440 | 1450 | 0.0001        | -               |
+| 1.7007 | 1500 | 0.0001        | -               |
+| 1.7574 | 1550 | 0.0001        | -               |
+| 1.8141 | 1600 | 0.0001        | -               |
+| 1.8707 | 1650 | 0.0001        | -               |
+| 1.9274 | 1700 | 0.0001        | -               |
+| 1.9841 | 1750 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.12.12
+- SetFit: 1.1.3
+- Sentence Transformers: 5.2.3
+- Transformers: 4.44.2
+- PyTorch: 2.10.0
+- Datasets: 4.6.0
+- Tokenizers: 0.19.1
+
+## 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": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.2.3",
+    "transformers": "4.44.2",
+    "pytorch": "2.10.0"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": [
+    "ablehnend",
+    "neutral",
+    "bef\u00fcrwortend"
+  ],
+  "normalize_embeddings": false
+}

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modules.json

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sentence_bert_config.json

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+    "max_seq_length": 128,
+    "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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