Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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: 'En rask, munter Dreng, af godt Udvortes, som har Lyst til Urtekramhandelen,
+    og er bekjendt med samme, kan erholde Emploi strax eller til April. Adressecomptoiret
+    modtager Billet desangaaende, under Mærke : Emploi Nr. 111, hvori ønskes opgivet,
+    hvor han har haft Leilighed til at gjøre sig bekjendt med samme.'
+- text: En Pige ønsker Condition strax eller til Nytaar hos 2 enlige Borgerfolk, hun
+    forstaaer et borgerligt Kiøkken, og kan godt spinde, hun er at unde i Aabenraa
+    i Kielderen 217.
+- text: En Stuepige, som forstaaer hvad hun bør, søger til Paaske; er at finde i Dronningens
+    Tvergade Nr. 363 i Stuen.
+- text: En Jomfrue søger Condition hos et Herskab eller honette Borgerfolk enten strax
+    eller til Paaske for at gaae i Huusholdningen, da hun tillige og kan den Pyndt
+    som udkræves til en Dames Opvartning; nærmere Efterretning gives paa AdresseContoiret.
+- text: Formedelst Sygdom er en Tieneste ledig for en Pige som kan malke, men uden
+    godt Skudsmaal nytter det ikke at melde sig; Anviisningengives i Gothersgaden
+    15.
+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: 0.9923954372623575
+      name: Accuracy
+    - type: f1
+      value: 0.9943820224719101
+      name: F1
+    - type: precision
+      value: 0.9943820224719101
+      name: Precision
+    - type: recall
+      value: 0.9943820224719101
+      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'En skikkelig Pige søger Condition strar eller til St. Hansdag som Opvartningspige, i Mangel deraf som Stue= eller Enevighvor Konen gaaer i Huusholdningen, anvises paa Hiørnet af Larsbjørnstræde og Volden 236 i Stuen.'</li><li>'En Pige fra Landet søger strax Condition for Amme eller Goldamme, er at finde paa Vesterbro Nr. 9.'</li><li>'En Kone, der godt kan vaske, stryge og tillige godt lave Mad, ønsker sig Condition hos en honet Familie som Kokke eller Enepige, eller og at gaae i ugeviis, hun kan tillige i malke, om forlanges, anvises i Bredgaden Nr. 202 paa 5 første Sal.'</li></ul>                                                                    |
+| 0     | <ul><li>'En skikkelig Karl fra Jylland søger Condition til St. Hansdag og er at finde paa Christianshavn paa Hiørnet af Dronningensgade og Torvegagen i Kielderen i Nr. 359.'</li><li>'En svensk Karl, nyelig kommen her til Staden, ønsker sig Condition som Kudsk eller Tienercher i Byen, eller paa Landet, har sine behørige Skudsmaal, er at finde i store Kongensgade No. 51.'</li><li>'En Student, der er øvet i at informere, tilbyder sig at give Underviisning i det tydske Sprog, Regning, Skrivning, Religion, samt andre til Akademiet hørende Videnskaber Anviisningen gives i Adelgaden Nr. 206, første Sal, det første Huus paa høire Haand fra Gottersgaden.'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy | F1     | Precision | Recall |
+|:--------|:---------|:-------|:----------|:-------|
+| **all** | 0.9924   | 0.9944 | 0.9944    | 0.9944 |
+
+## 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("En Stuepige, som forstaaer hvad hun bør, søger til Paaske; er at finde i Dronningens Tvergade Nr. 363 i Stuen.")
+```
+
+<!--
+### 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   | 8   | 32.4388 | 176 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 194                   |
+| 1     | 419                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0011 | 1    | 0.2907        | -               |
+| 0.0543 | 50   | 0.2618        | -               |
+| 0.1087 | 100  | 0.0493        | -               |
+| 0.1630 | 150  | 0.0181        | -               |
+| 0.2174 | 200  | 0.0038        | -               |
+| 0.2717 | 250  | 0.001         | -               |
+| 0.3261 | 300  | 0.0005        | -               |
+| 0.3804 | 350  | 0.0003        | -               |
+| 0.4348 | 400  | 0.0002        | -               |
+| 0.4891 | 450  | 0.0001        | -               |
+| 0.5435 | 500  | 0.0001        | -               |
+| 0.5978 | 550  | 0.0001        | -               |
+| 0.6522 | 600  | 0.0001        | -               |
+| 0.7065 | 650  | 0.0001        | -               |
+| 0.7609 | 700  | 0.0001        | -               |
+| 0.8152 | 750  | 0.0001        | -               |
+| 0.8696 | 800  | 0.0001        | -               |
+| 0.9239 | 850  | 0.0           | -               |
+| 0.9783 | 900  | 0.0           | -               |
+| 1.0326 | 950  | 0.0           | -               |
+| 1.0870 | 1000 | 0.0           | -               |
+| 1.1413 | 1050 | 0.0           | -               |
+| 1.1957 | 1100 | 0.0           | -               |
+| 1.25   | 1150 | 0.0           | -               |
+| 1.3043 | 1200 | 0.0           | -               |
+| 1.3587 | 1250 | 0.0           | -               |
+| 1.4130 | 1300 | 0.0           | -               |
+| 1.4674 | 1350 | 0.0           | -               |
+| 1.5217 | 1400 | 0.0           | -               |
+| 1.5761 | 1450 | 0.0           | -               |
+| 1.6304 | 1500 | 0.0           | -               |
+| 1.6848 | 1550 | 0.0           | -               |
+| 1.7391 | 1600 | 0.0           | -               |
+| 1.7935 | 1650 | 0.0           | -               |
+| 1.8478 | 1700 | 0.0           | -               |
+| 1.9022 | 1750 | 0.0           | -               |
+| 1.9565 | 1800 | 0.0           | -               |
+| 2.0109 | 1850 | 0.0           | -               |
+| 2.0652 | 1900 | 0.0           | -               |
+| 2.1196 | 1950 | 0.0           | -               |
+| 2.1739 | 2000 | 0.0           | -               |
+| 2.2283 | 2050 | 0.0           | -               |
+| 2.2826 | 2100 | 0.0           | -               |
+| 2.3370 | 2150 | 0.0           | -               |
+| 2.3913 | 2200 | 0.0           | -               |
+| 2.4457 | 2250 | 0.0           | -               |
+| 2.5    | 2300 | 0.0           | -               |
+| 2.5543 | 2350 | 0.0           | -               |
+| 2.6087 | 2400 | 0.0           | -               |
+| 2.6630 | 2450 | 0.0           | -               |
+| 2.7174 | 2500 | 0.0           | -               |
+| 2.7717 | 2550 | 0.0           | -               |
+| 2.8261 | 2600 | 0.0           | -               |
+| 2.8804 | 2650 | 0.0           | -               |
+| 2.9348 | 2700 | 0.0           | -               |
+| 2.9891 | 2750 | 0.0           | -               |
+
+### 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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+[
+  {
+    "idx": 0,
+    "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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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "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_lower_case": true,
+  "extra_special_tokens": {},
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+  "padding_side": "right",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}