Push model using huggingface_hub.

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "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: 1) Niels Budelsen, svensk Arbejdsmand, nogle og 20 Aar gl., middelhøj, lyst
+    Haar, intet Skjæg, iført enten graabrune hvergarns Klæder eller graa Buckskins
+    Benklæder og Vestengelsk Hue eller sort rundpullet Hat og skident hvidt Halstørklæde,
+    sigtes for Tyveri. Anholdes hertil. (St. 2, 770.)
+- text: 1) Hans Hansen, Arbejdsmand, født den 12/12 52 Fuldby, middel af Højde og
+    Bygning, rødt Hageskjæg, er igaar undvegen fra Frederiksberg Fattighus. Ved Borgangen
+    var han iført graa islandsk Nattrøje, gl. mørk Vest, sorte Benklæder, LærredsSkjorte,
+    mrk. F. F., flad graa Kaskjet og Træsko. Anholdes til K. A. søndre Birk.
+- text: 3) Kolportør Olsen, der rejser for N. C. Rom og formentlig for Tiden rejser
+    i det nordlige Jylland, eftersøges i Anledning af at han har forladt sit Logis
+    uden at betale. I Antræffelsestilfælde udbedes Underretning om hans Opholdsted
+    til Byfogden i Vejle, hvorefter Begjæring om hans Afhøring vil blive fremsendt.
+- text: 3) Pigen Dagmar Schrøder, Datter af Privatvægter Frederik Schrøder, Istedgade
+    6, 2. Sal, er den 24. Ds. bortgaaet fra Hjemmet. Hun er 12. Aar gl., svær af Bygning,
+    har blondt Haar (Pandehaar, var iført rødbrun Nederdel, sort Liv, Sko og Sivhat
+    med Blondebesætning. (H. St.)
+- text: 2) Lauriz Christian Carl Mariager, omtrent 29 Aar gl., født i Kjøbenhavn,
+    over Middelhøjde, med mørkt Haar, lidt Overskjæg, smalt blegt Ansigt, iført blaa
+    Stortrøje, Benklæder, blaa engelsk Kaskjet med lige udstaaende Skygge, sigtes
+    for Bedrageri. Anh. hertil. (St. 2. 1025.)
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
+model-index:
+- name: SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9665178571428571
+      name: Accuracy
+    - type: f1
+      value: 0.9794801641586868
+      name: F1
+    - type: precision
+      value: 0.9701897018970189
+      name: Precision
+    - type: recall
+      value: 0.988950276243094
+      name: Recall
+---
+
+# SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1) 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:** [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1)
+- **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'3) Da den i P. E. 146-7 ommeldte Bagersvend, Theodor Victor Holst Wildenrath endnu ikke er anholdt, gjentages Efterlysningen. (Falsters vestre Herred.'</li><li>'6) Robert Oscar Charles Friederichsen, født den 14 Juni 1874 i Kjøbenhavn, der ved Kjøbenhavns Kriminal- og Politirets Dom af 5 April et dømt til F. paa sædv. Fgk. i 14 Dage for Tyveri, er derefter udvandret til Buenos Ayres. Saafremt han atter skulde vende tilbage, bedes han anholdt. og Underretning til Kjøbenhavns Politi.'</li><li>'3) Hattefabrikant Vilhelm Lund af Aarhus, der d. 6. Ds. forlod Aarhuus pr. Jernbane paa en Forretningsrejse til Kjøbenhavn, er ikke senere ankommen hertil. Den 10. Ds, skal han have været i Fredericia. Underretning om hans Opholdsted hertil. (H. St.)'</li></ul> |
+| 1     | <ul><li>'3) En Mandsperson, nogle og 20 Aar gl., middel at Højde og Bygning, lyst Haar, intet Skjæg, iført blaat Tøj og Kaskjet med blank Skygge, og er noget tunghør, sigtes for Tyveri. (St. 2).'</li><li>'6) En Sømandsdreng ved Navn Niels, 17 a 18 Aar gl., formentlig hjemmehørende i Randers, blond, middel af Højde og Bygning, iført blaa Jakkeklædning og flad Kaskjet med blanke Knapper, sigtes for Tyveri ombord i Skib. (St. 1, 917).'</li><li>'1) Henning William Andresen, født i Kbhvn., 13 Aar gl., spinkel af Bygning, mørkt Haar, brune Øine og bleg Ansigtsfarve, iført sort Klædes Trøie og Vest, mørke prikkede Benklæder og Støvler, – er den 4de ds. bortgaaet fra sit Hjem. (III).'</li></ul>                                                                         |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy | F1     | Precision | Recall |
+|:--------|:---------|:-------|:----------|:-------|
+| **all** | 0.9665   | 0.9795 | 0.9702    | 0.9890 |
+
+## 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("3) Pigen Dagmar Schrøder, Datter af Privatvægter Frederik Schrøder, Istedgade 6, 2. Sal, er den 24. Ds. bortgaaet fra Hjemmet. Hun er 12. Aar gl., svær af Bygning, har blondt Haar (Pandehaar, var iført rødbrun Nederdel, sort Liv, Sko og Sivhat med Blondebesætning. (H. St.)")
+```
+
+<!--
+### 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   | 7   | 55.4928 | 497 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 208                   |
+| 1     | 835                   |
+
+### Training Hyperparameters
+- batch_size: (24, 24)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 12
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0010 | 1    | 0.255         | -               |
+| 0.0479 | 50   | 0.136         | -               |
+| 0.0959 | 100  | 0.0552        | -               |
+| 0.1438 | 150  | 0.0385        | -               |
+| 0.1918 | 200  | 0.0237        | -               |
+| 0.2397 | 250  | 0.0175        | -               |
+| 0.2876 | 300  | 0.014         | -               |
+| 0.3356 | 350  | 0.0096        | -               |
+| 0.3835 | 400  | 0.0088        | -               |
+| 0.4314 | 450  | 0.0101        | -               |
+| 0.4794 | 500  | 0.008         | -               |
+| 0.5273 | 550  | 0.0051        | -               |
+| 0.5753 | 600  | 0.0036        | -               |
+| 0.6232 | 650  | 0.0006        | -               |
+| 0.6711 | 700  | 0.0002        | -               |
+| 0.7191 | 750  | 0.0001        | -               |
+| 0.7670 | 800  | 0.0002        | -               |
+| 0.8150 | 850  | 0.0001        | -               |
+| 0.8629 | 900  | 0.0001        | -               |
+| 0.9108 | 950  | 0.0001        | -               |
+| 0.9588 | 1000 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.11.12
+- SetFit: 1.1.3
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.7.0
+- Datasets: 2.19.2
+- Tokenizers: 0.21.1
+
+## 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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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.7.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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

model.safetensors

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model_head.pkl

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "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,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+      "lstrip": false,
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
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+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
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+  "pad_token": "[PAD]",
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+  "padding_side": "right",
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+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}