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: Etunimi Sukunimi Ruotsin kansasta yli puolet reilusti kannattaa Natoon hakemista.
+    Ainoana esteenä näkisin, että Ruotsin asevoimat eivät ole läheskään niin hyvällä
+    tolalla kuin Suomen mitä tulee varusmiehiin, reserviläisiin tai edes kalustoonkaan.
+    Mutta yhteisen hakemuksen kohdalla se tuskin olisi ongelma muille Nato-maille
+    hyväksynnän suhteen. Toinen ongelma on, että hyväksynnän tulisi olla sataprosenttinen
+    ja ei ole poissujettua, että Venäjä esmes vaikuttaisi yksittäiseen maahan niin,
+    että juuri se ei hyväksyisikään hakemusta.
+- text: Etunimi Pugh nyt ymmärrän sun puolustelut asut jenkeissä.....
+- text: Etunimi Sukunimi ei varmasti moni uskalla
+- text: Etunimi Sukunimi Voisitko laittaa tuohon lastentappoväitteeseen mukaan jonkinlaista
+    faktaa. Jää muuten melko irralliseksi heitoksi. Ja etkös aiemmin korostanut, että
+    maa ei ole sama kuin ihmiset? No mikä on maa tai valtio, se on jäsentensä muodostama.
+    Nyt sitten väität, että Ukraina on maana tappanut lapsia 8 vuotta.
+- text: Etunimi Sukunimi Historiaa kirjoitetaan vielä maaliskuun 2020 tapahtumista.
+metrics:
+- metric
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: TurkuNLP/bert-base-finnish-cased-v1
+model-index:
+- name: SetFit with TurkuNLP/bert-base-finnish-cased-v1
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: metric
+      value: 0.9395602682052082
+      name: Metric
+---
+
+# SetFit with TurkuNLP/bert-base-finnish-cased-v1
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) 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:** [TurkuNLP/bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1)
+- **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>'Etunimi Raines jep. Suomalainen rokottamaton paha, ukrainalainen rokottamaton hyvä. Tää näkyy olevan nyt se mentaliteetti tällä hetkellä...'</li><li>'Etunimi Bistrom tilastot.'</li><li>'Etunimi Sukunimi myös delta oli suurimmalle osalle myös rokottamattomille lähes oireeton, omikron kuulemma vielä lievempi👏'</li></ul>                                                                                                                                                                                                                                                                                                                                                                          |
+| 1     | <ul><li>'Etunimi Sukunimi katoin ihan samaa  tauti lisääntyy samaa tahtii ku piikitkii'</li><li>'Etunimi Sukunimi näinhän se on, ja uskon ja toivon että nyt Suomessakin ymmärretään että mitä putin tarkoitti kun sanoi puolustavansa kaikkia venäläisiä, riippumatta heidän kansallisuudestaan. Täällä Suomessa pitää nyt ottaa myös tarkasteluun että mikä merkitys suomen venäläisvähemmistöllä ja venäläisten maaomistuksilla on Suomessa. Vaikka suomen venäläiset eivät ikimaailmassa olisi Putinin puolella, he ovat merkittävä vipuvarsi Putinille kun hän perustelee mahdollisia toimia Suomen valtiota vastaan.'</li><li>'Etunimi Sukunimi tuohon kiinnitin minäkin huomiota ensimmäiseksi.'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Metric |
+|:--------|:-------|
+| **all** | 0.9396 |
+
+## 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("Finnish-actions/SetFit-FinBERT1-A3-acceptance")
+# Run inference
+preds = model("Etunimi Sukunimi ei varmasti moni uskalla")
+```
+
+<!--
+### 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   | 19.6854 | 213 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 874                   |
+| 1     | 89                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (4, 4)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 6
+- body_learning_rate: (2e-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
+- l2_weight: 0.01
+- seed: 42
+- evaluation_strategy: epoch
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0014 | 1    | 0.2574        | -               |
+| 0.0692 | 50   | 0.274         | -               |
+| 0.1383 | 100  | 0.2502        | -               |
+| 0.2075 | 150  | 0.2157        | -               |
+| 0.2766 | 200  | 0.1361        | -               |
+| 0.3458 | 250  | 0.0552        | -               |
+| 0.4149 | 300  | 0.0205        | -               |
+| 0.4841 | 350  | 0.0174        | -               |
+| 0.5533 | 400  | 0.0022        | -               |
+| 0.6224 | 450  | 0.0085        | -               |
+| 0.6916 | 500  | 0.0054        | -               |
+| 0.7607 | 550  | 0.0042        | -               |
+| 0.8299 | 600  | 0.0005        | -               |
+| 0.8990 | 650  | 0.0016        | -               |
+| 0.9682 | 700  | 0.0013        | -               |
+| 1.0    | 723  | -             | 0.1899          |
+| 1.0373 | 750  | 0.0016        | -               |
+| 1.1065 | 800  | 0.0074        | -               |
+| 1.1757 | 850  | 0.0022        | -               |
+| 1.2448 | 900  | 0.001         | -               |
+| 1.3140 | 950  | 0.0001        | -               |
+| 1.3831 | 1000 | 0.0073        | -               |
+| 1.4523 | 1050 | 0.0037        | -               |
+| 1.5214 | 1100 | 0.0046        | -               |
+| 1.5906 | 1150 | 0.0025        | -               |
+| 1.6598 | 1200 | 0.0027        | -               |
+| 1.7289 | 1250 | 0.0035        | -               |
+| 1.7981 | 1300 | 0.0021        | -               |
+| 1.8672 | 1350 | 0.0053        | -               |
+| 1.9364 | 1400 | 0.0034        | -               |
+| 2.0    | 1446 | -             | 0.2088          |
+| 2.0055 | 1450 | 0.0082        | -               |
+| 2.0747 | 1500 | 0.0059        | -               |
+| 2.1438 | 1550 | 0.0024        | -               |
+| 2.2130 | 1600 | 0.0005        | -               |
+| 2.2822 | 1650 | 0.0049        | -               |
+| 2.3513 | 1700 | 0.0029        | -               |
+| 2.4205 | 1750 | 0.0045        | -               |
+| 2.4896 | 1800 | 0.0031        | -               |
+| 2.5588 | 1850 | 0.002         | -               |
+| 2.6279 | 1900 | 0.0023        | -               |
+| 2.6971 | 1950 | 0.0053        | -               |
+| 2.7663 | 2000 | 0.0015        | -               |
+| 2.8354 | 2050 | 0.0025        | -               |
+| 2.9046 | 2100 | 0.0009        | -               |
+| 2.9737 | 2150 | 0.0015        | -               |
+| 3.0    | 2169 | -             | 0.1619          |
+| 3.0429 | 2200 | 0.0059        | -               |
+| 3.1120 | 2250 | 0.0034        | -               |
+| 3.1812 | 2300 | 0.0027        | -               |
+| 3.2503 | 2350 | 0.0038        | -               |
+| 3.3195 | 2400 | 0.0014        | -               |
+| 3.3887 | 2450 | 0.0034        | -               |
+| 3.4578 | 2500 | 0.0049        | -               |
+| 3.5270 | 2550 | 0.0024        | -               |
+| 3.5961 | 2600 | 0.0025        | -               |
+| 3.6653 | 2650 | 0.0025        | -               |
+| 3.7344 | 2700 | 0.0044        | -               |
+| 3.8036 | 2750 | 0.0004        | -               |
+| 3.8728 | 2800 | 0.0002        | -               |
+| 3.9419 | 2850 | 0.0012        | -               |
+| 4.0    | 2892 | -             | 0.1642          |
+
+### Framework Versions
+- Python: 3.11.9
+- SetFit: 1.1.3
+- Sentence Transformers: 3.2.0
+- Transformers: 4.44.0
+- PyTorch: 2.4.0+cu124
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## 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

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "TurkuNLP/bert-base-finnish-cased-v1",
+  "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": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50105
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.0",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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+size 498110312

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": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

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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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
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+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}