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.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
+my-receipt-categorizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text

my-receipt-categorizer/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
+}

my-receipt-categorizer/README.md

@@ -0,0 +1,187 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Książka
+- text: Zestaw obiadowy
+- text: Ser Gouda plastry
+- text: Olej napędowy ON
+- text: Whisky Jack Daniels
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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:** 7 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                                                                                                     |
+|:------------------------|:-------------------------------------------------------------------------------------------------------------|
+| Groceries               | <ul><li>'Mleko 3.2% Łaciate'</li><li>'Chleb wiejski krojony'</li><li>'Masło Extra'</li></ul>                 |
+| Alcohol and stimulants  | <ul><li>'Piwo Tyskie 0.5L'</li><li>'Piwo Żywiec'</li><li>'Piwo Kraftowe IPA'</li></ul>                       |
+| Household and chemistry | <ul><li>'Domestos 1L'</li><li>'Papier toaletowy 8 rolek'</li><li>'Płyn do naczyń Ludwik'</li></ul>           |
+| Cosmetics               | <ul><li>'Szampon Head&Shoulders'</li><li>'Żel pod prysznic Nivea'</li><li>'Pasta do zębów Colgate'</li></ul> |
+| Entertainment           | <ul><li>'Bilet do kina'</li><li>'Gra na PS5'</li><li>'Książka'</li></ul>                                     |
+| Transport               | <ul><li>'Bilet autobusowy'</li><li>'Bilet ulgowy MPK'</li><li>'Benzyna PB95'</li></ul>                       |
+| Ignore                  | <ul><li>'Torba foliowa'</li><li>'Reklamówka'</li><li>'Siateczka'</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("Książka")
+```
+
+<!--
+### 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   | 2.2966 | 4   |
+
+| Label                   | Training Sample Count |
+|:------------------------|:----------------------|
+| Alcohol and stimulants  | 22                    |
+| Cosmetics               | 14                    |
+| Entertainment           | 12                    |
+| Groceries               | 28                    |
+| Household and chemistry | 15                    |
+| Ignore                  | 16                    |
+| Transport               | 11                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 5
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0135 | 1    | 0.192         | -               |
+| 0.6757 | 50   | 0.1392        | -               |
+
+### Framework Versions
+- Python: 3.10.15
+- SetFit: 1.1.3
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.3
+- PyTorch: 2.9.1+cu128
+- Datasets: 4.4.1
+- Tokenizers: 0.22.1
+
+## 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.*
+-->

my-receipt-categorizer/config.json

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+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 12,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

my-receipt-categorizer/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.3",
+    "pytorch": "2.9.1+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

my-receipt-categorizer/config_setfit.json

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+{
+  "labels": [
+    "Alcohol and stimulants",
+    "Cosmetics",
+    "Entertainment",
+    "Groceries",
+    "Household and chemistry",
+    "Ignore",
+    "Transport"
+  ],
+  "normalize_embeddings": false
+}

my-receipt-categorizer/model.safetensors

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my-receipt-categorizer/model_head.pkl

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my-receipt-categorizer/modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

my-receipt-categorizer/sentence_bert_config.json

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+    "do_lower_case": false
+}

my-receipt-categorizer/sentencepiece.bpe.model

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my-receipt-categorizer/special_tokens_map.json

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my-receipt-categorizer/tokenizer.json

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my-receipt-categorizer/tokenizer_config.json

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+}