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README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Tennengebirge Reef
+- text: Outcrop next to I-84 East
+- text: scenic overview
+- text: Ruby Star for sale now Please contact us for more details. Regards
+- text: torre rocosa de grans dimensions. 3 blocs partits
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: BAAI/bge-small-en-v1.5
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
+- **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>'Calcite'</li><li>'biotite. Contact metamorphosis'</li><li>'rail trail'</li></ul>                                         |
+| 1     | <ul><li>'Geafitti on tree and burn scar on ground'</li><li>'another beautiful rock from the same place'</li><li>'Vhfgv'</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("scenic overview")
+```
+
+<!--
+### 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   | 7.2788 | 1899 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 2997                  |
+| 1     | 783                   |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- 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
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0002 | 1    | 0.2331        | -               |
+| 0.0106 | 50   | 0.2391        | -               |
+| 0.0212 | 100  | 0.238         | -               |
+| 0.0317 | 150  | 0.2309        | -               |
+| 0.0423 | 200  | 0.2117        | -               |
+| 0.0529 | 250  | 0.1879        | -               |
+| 0.0635 | 300  | 0.1745        | -               |
+| 0.0741 | 350  | 0.1708        | -               |
+| 0.0847 | 400  | 0.1402        | -               |
+| 0.0952 | 450  | 0.1349        | -               |
+| 0.1058 | 500  | 0.1092        | -               |
+| 0.1164 | 550  | 0.1031        | -               |
+| 0.1270 | 600  | 0.0828        | -               |
+| 0.1376 | 650  | 0.0756        | -               |
+| 0.1481 | 700  | 0.0587        | -               |
+| 0.1587 | 750  | 0.0487        | -               |
+| 0.1693 | 800  | 0.0557        | -               |
+| 0.1799 | 850  | 0.0456        | -               |
+| 0.1905 | 900  | 0.0371        | -               |
+| 0.2011 | 950  | 0.0412        | -               |
+| 0.2116 | 1000 | 0.0382        | -               |
+| 0.2222 | 1050 | 0.0376        | -               |
+| 0.2328 | 1100 | 0.0353        | -               |
+| 0.2434 | 1150 | 0.0346        | -               |
+| 0.2540 | 1200 | 0.0364        | -               |
+| 0.2646 | 1250 | 0.0317        | -               |
+| 0.2751 | 1300 | 0.0374        | -               |
+| 0.2857 | 1350 | 0.0282        | -               |
+| 0.2963 | 1400 | 0.0255        | -               |
+| 0.3069 | 1450 | 0.023         | -               |
+| 0.3175 | 1500 | 0.0287        | -               |
+| 0.3280 | 1550 | 0.025         | -               |
+| 0.3386 | 1600 | 0.0216        | -               |
+| 0.3492 | 1650 | 0.0241        | -               |
+| 0.3598 | 1700 | 0.0234        | -               |
+| 0.3704 | 1750 | 0.0279        | -               |
+| 0.3810 | 1800 | 0.0239        | -               |
+| 0.3915 | 1850 | 0.0199        | -               |
+| 0.4021 | 1900 | 0.0252        | -               |
+| 0.4127 | 1950 | 0.0219        | -               |
+| 0.4233 | 2000 | 0.0228        | -               |
+| 0.4339 | 2050 | 0.0204        | -               |
+| 0.4444 | 2100 | 0.0231        | -               |
+| 0.4550 | 2150 | 0.0144        | -               |
+| 0.4656 | 2200 | 0.0229        | -               |
+| 0.4762 | 2250 | 0.0129        | -               |
+| 0.4868 | 2300 | 0.0219        | -               |
+| 0.4974 | 2350 | 0.0194        | -               |
+| 0.5079 | 2400 | 0.0172        | -               |
+| 0.5185 | 2450 | 0.0177        | -               |
+| 0.5291 | 2500 | 0.0252        | -               |
+| 0.5397 | 2550 | 0.0251        | -               |
+| 0.5503 | 2600 | 0.014         | -               |
+| 0.5608 | 2650 | 0.0204        | -               |
+| 0.5714 | 2700 | 0.0248        | -               |
+| 0.5820 | 2750 | 0.0146        | -               |
+| 0.5926 | 2800 | 0.0191        | -               |
+| 0.6032 | 2850 | 0.0223        | -               |
+| 0.6138 | 2900 | 0.0206        | -               |
+| 0.6243 | 2950 | 0.0163        | -               |
+| 0.6349 | 3000 | 0.0235        | -               |
+| 0.6455 | 3050 | 0.0245        | -               |
+| 0.6561 | 3100 | 0.0199        | -               |
+| 0.6667 | 3150 | 0.0145        | -               |
+| 0.6772 | 3200 | 0.016         | -               |
+| 0.6878 | 3250 | 0.0143        | -               |
+| 0.6984 | 3300 | 0.0206        | -               |
+| 0.7090 | 3350 | 0.0187        | -               |
+| 0.7196 | 3400 | 0.0168        | -               |
+| 0.7302 | 3450 | 0.0148        | -               |
+| 0.7407 | 3500 | 0.0212        | -               |
+| 0.7513 | 3550 | 0.0185        | -               |
+| 0.7619 | 3600 | 0.015         | -               |
+| 0.7725 | 3650 | 0.0187        | -               |
+| 0.7831 | 3700 | 0.0161        | -               |
+| 0.7937 | 3750 | 0.0204        | -               |
+| 0.8042 | 3800 | 0.0182        | -               |
+| 0.8148 | 3850 | 0.0157        | -               |
+| 0.8254 | 3900 | 0.0197        | -               |
+| 0.8360 | 3950 | 0.0133        | -               |
+| 0.8466 | 4000 | 0.0211        | -               |
+| 0.8571 | 4050 | 0.0155        | -               |
+| 0.8677 | 4100 | 0.0197        | -               |
+| 0.8783 | 4150 | 0.0168        | -               |
+| 0.8889 | 4200 | 0.0139        | -               |
+| 0.8995 | 4250 | 0.0132        | -               |
+| 0.9101 | 4300 | 0.018         | -               |
+| 0.9206 | 4350 | 0.014         | -               |
+| 0.9312 | 4400 | 0.017         | -               |
+| 0.9418 | 4450 | 0.0173        | -               |
+| 0.9524 | 4500 | 0.0163        | -               |
+| 0.9630 | 4550 | 0.0178        | -               |
+| 0.9735 | 4600 | 0.0176        | -               |
+| 0.9841 | 4650 | 0.0126        | -               |
+| 0.9947 | 4700 | 0.0194        | -               |
+
+### Framework Versions
+- Python: 3.12.9
+- SetFit: 1.1.2
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.4
+- PyTorch: 2.7.1
+- Datasets: 3.6.0
+- 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": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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": "4.52.4",
+  "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": "4.52.4",
+    "pytorch": "2.7.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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

model.safetensors

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+size 133462128

model_head.pkl

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+size 3935

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

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