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JohanHeinsen
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Labour_ads_demand

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +252 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +65 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ "include_prompt": true
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README.md ADDED
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+ ---
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: En Kokkepige søger Condition hos et Herskab eller honette Borgerfolk, enten
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+ strax eller til St. Hansdag, anvises i Adelgaden No. 287, første Bagsal.
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+ - text: En anstændig Pige ønsker Condition som Stue, Kokke= eller Ene=Pige. Hun forstaaer
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+ Hvad hun paatage sig og recommenderes fra det Sted, hun nu tjener, paa Hjørnet
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+ af Raadhuusstrædet og Brolæggerstrædet Nr. 46, anden Sal.
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+ - text: Et anstændig Fruentimmer som i flere Aar har tient i store Landhuusholdninger,
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+ ønsker sig en saadan Tieneste igien til 1ste November.
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+ - text: En Pige søger Tieneste hos eenlige Folk, eller hvor der er et Par Børn at
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+ passe, anvises i lille Færgestrædet Nr. 231.
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+ - text: Formedelst Sygdom er en Tieneste ledig for en Pige som kan malke, men uden
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+ godt Skudsmaal nytter det ikke at melde sig; Anviisningengives i Gothersgaden
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+ 15.
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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+ model-index:
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+ - name: SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.9433962264150944
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+ name: Accuracy
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+ - type: f1
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+ value: 0.9238578680203046
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+ name: F1
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+ - type: precision
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+ value: 0.8921568627450981
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+ name: Precision
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+ - type: recall
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+ value: 0.9578947368421052
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+ name: Recall
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+ ---
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+
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+ # SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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+
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+ 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 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 Amme søger Plads, eller i Mangel som Goldamme, er at finde i Nyehavn, anden Port fra Charlottenborg.'</li><li>'En skikkelig Pige, som kan forevise de bedste Skudsmaale, ønsker sig en Tieneste som Frøkenpige eller Stuepige til 1ste Novbr. enten paa en Herregaard eller hos en honet Familie i Kiøbstæden. Hun anvises fra Adressecomtoiret.'</li></ul> |
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+ | 1 | <ul><li>'En skikkelig Jomfru, som forstaaer godt Madlavning, Vadsk, Reengjøren og deslige, kan faae Condition paa Vesterbro Nr. 63, men uden gode Recommendationer om Troskab og god Opførsel nytter det ikke at mælde sig.'</li><li>'En Pige, som kan paatage sig et Kjøkken, kan strax faae Condition, naar hun mælder sig i Toldbodgaden Nr. 44, i Stuen.'</li><li>'En Goldamme kan strax faae Condition i Kronprindsensgaden Nr. 39, 3die Sal.'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy | F1 | Precision | Recall |
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+ |:--------|:---------|:-------|:----------|:-------|
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+ | **all** | 0.9434 | 0.9239 | 0.8922 | 0.9579 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("setfit_model_id")
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+ # Run inference
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+ preds = model("En Pige søger Tieneste hos eenlige Folk, eller hvor der er et Par Børn at passe, anvises i lille Færgestrædet Nr. 231.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 8 | 32.1640 | 176 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 389 |
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+ | 1 | 227 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (2, 2)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 12
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0011 | 1 | 0.0621 | - |
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+ | 0.0541 | 50 | 0.2937 | - |
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+ | 0.1082 | 100 | 0.1367 | - |
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+ | 0.1623 | 150 | 0.037 | - |
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+ | 0.2165 | 200 | 0.0215 | - |
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+ | 0.2706 | 250 | 0.0165 | - |
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+ | 0.3247 | 300 | 0.0103 | - |
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+ | 0.3788 | 350 | 0.0134 | - |
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+ | 0.4329 | 400 | 0.0146 | - |
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+ | 0.4870 | 450 | 0.003 | - |
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+ | 0.5411 | 500 | 0.0028 | - |
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+ | 0.5952 | 550 | 0.0027 | - |
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+ | 0.6494 | 600 | 0.0039 | - |
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+ | 0.7035 | 650 | 0.0003 | - |
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+ | 0.7576 | 700 | 0.0001 | - |
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+ | 0.8117 | 750 | 0.0001 | - |
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+ | 0.8658 | 800 | 0.0001 | - |
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+ | 0.9199 | 850 | 0.0001 | - |
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+ | 0.9740 | 900 | 0.0 | - |
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+ | 1.0281 | 950 | 0.0 | - |
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+ | 1.0823 | 1000 | 0.0 | - |
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+ | 1.1364 | 1050 | 0.0 | - |
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+ | 1.1905 | 1100 | 0.0 | - |
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+ | 1.2446 | 1150 | 0.0 | - |
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+ | 1.2987 | 1200 | 0.0 | - |
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+ | 1.3528 | 1250 | 0.0 | - |
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+ | 1.4069 | 1300 | 0.0 | - |
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+ | 1.4610 | 1350 | 0.0 | - |
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+ | 1.5152 | 1400 | 0.0 | - |
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+ | 1.5693 | 1450 | 0.0 | - |
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+ | 1.6234 | 1500 | 0.0 | - |
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+ | 1.6775 | 1550 | 0.0 | - |
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+ | 1.7316 | 1600 | 0.0 | - |
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+ | 1.7857 | 1650 | 0.0 | - |
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+ | 1.8398 | 1700 | 0.0 | - |
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+ | 1.8939 | 1750 | 0.0 | - |
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+ | 1.9481 | 1800 | 0.0 | - |
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+
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+ ### Framework Versions
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+ - Python: 3.11.12
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+ - SetFit: 1.1.3
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+ - Sentence Transformers: 4.1.0
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+ - Transformers: 4.51.3
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+ - PyTorch: 2.7.0
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+ - Datasets: 2.19.2
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+ - Tokenizers: 0.21.1
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+
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+ ## Citation
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+
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+ ### BibTeX
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+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
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+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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