Push model using huggingface_hub.

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

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: für Integration
+- text: Zugang zu Integrationsmaßnahmen sicherstellen;
+- text: Wir sehen in der natürlichen Zwei- oder Mehrsprachigkeit ein wichtiges Potenzial,
+    das durch eine gezielte sprachliche Förderung realisiert werden kann.
+- text: Deutschland braucht ein umfassendes Integrationskonzept auf allen Ebenen -
+    der Kommune, des Landes und des Bundes.
+- text: Eine offene Gesellschaft bietet im Rahmen der Grundrechte allen Religionen
+    den Freiraum zur Entfaltung ihres Glaubens.
+metrics:
+- f1
+- precision
+- recall
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: deutsche-telekom/gbert-large-paraphrase-cosine
+model-index:
+- name: SetFit with deutsche-telekom/gbert-large-paraphrase-cosine
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: f1
+      value: 0.8563995837669095
+      name: F1
+    - type: precision
+      value: 0.858476507713885
+      name: Precision
+    - type: recall
+      value: 0.8548387096774194
+      name: Recall
+---
+
+# SetFit with deutsche-telekom/gbert-large-paraphrase-cosine
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [deutsche-telekom/gbert-large-paraphrase-cosine](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine) 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:** [deutsche-telekom/gbert-large-paraphrase-cosine](https://huggingface.co/deutsche-telekom/gbert-large-paraphrase-cosine)
+- **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                                                                                                                                                                                                                                                                                                                                            |
+|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'Deutschland ist ein gastfreundliches und weltoffenes Land.'</li><li>'Aber auch in der Polizei und Justiz muss sich einiges ändern.'</li><li>'Die FDP sucht das Gespräch mit der evangelischen und katholischen Kirche ebenso wie mit dem Judentum, dem Islam und allen anderen Religionsgemeinschaften.'</li></ul>                         |
+| 0     | <ul><li>'Wir wollen eine Integrationsoffensive.'</li><li>'Kenntnisse der deutschen Sprache sind eine unverzichtbare Voraussetzung zur Beseitigung sozialer Benachteiligungen und zum Erreichen schulischer, beruflicher und gesellschaftlicher Erfolge.'</li><li>'Wir erwarten von Zuwandernden, dass sie die deutsche Sprache erlernen.'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | F1     | Precision | Recall |
+|:--------|:-------|:----------|:-------|
+| **all** | 0.8564 | 0.8585    | 0.8548 |
+
+## 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("gehaustein/gbert-large-stance-multiculturalism")
+# Run inference
+preds = model("für Integration")
+```
+
+<!--
+### 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   | 14.6336 | 42  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 128                   |
+| 1     | 366                   |
+
+### Training Hyperparameters
+- batch_size: (128, 128)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (1e-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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch      | Step    | Training Loss | Validation Loss |
+|:----------:|:-------:|:-------------:|:---------------:|
+| 0.0008     | 1       | 0.3283        | -               |
+| 0.0424     | 50      | 0.2401        | 0.234           |
+| 0.0848     | 100     | 0.0852        | 0.202           |
+| 0.1272     | 150     | 0.0054        | 0.2493          |
+| 0.1696     | 200     | 0.001         | 0.2502          |
+| 0.2120     | 250     | 0.0002        | 0.2513          |
+| 0.2545     | 300     | 0.0012        | 0.2496          |
+| 0.2969     | 350     | 0.0046        | 0.2485          |
+| 0.3393     | 400     | 0.0056        | 0.2538          |
+| 0.3817     | 450     | 0.0001        | 0.2543          |
+| **0.4241** | **500** | **0.0001**    | **0.2443**      |
+| 0.4665     | 550     | 0.0001        | 0.2472          |
+| 0.5089     | 600     | 0.0051        | 0.2655          |
+| 0.5513     | 650     | 0.0002        | 0.2646          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.48.1
+- PyTorch: 2.5.1+cu121
+- Datasets: 2.14.4
+- 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": "checkpoints/step_500",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.1",
+    "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": null
+}

model.safetensors

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+size 1342988112

model_head.pkl

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modules.json

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+    "name": "0",
+    "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": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
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+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "single_word": false
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+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "max_length": 512,
+  "model_max_length": 512,
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+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}