Upload folder using huggingface_hub

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "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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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: sentence-transformers/all-MiniLM-L6-v2
+metrics:
+- accuracy
+widget:
+- text: Education is very important for children in poverty and can also create a
+    future
+- text: Sugandhi Devi has two daughters and she does not have a bicycle for their
+    education
+- text: In this discussion we will tell that what should be done when the girl child
+    is not accompanied by her parents she will have to study a little at least
+- text: A community meeting will be held in which a resolution will be taken to stop
+    gender discrimination
+- text: The childrens mother said that she would go to Bodh Gaya block to get her
+    Aadhaar card made
+pipeline_tag: text-classification
+inference: true
+---
+
+# SetFit with sentence-transformers/all-MiniLM-L6-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 256 tokens
+- **Number of Classes:** 3 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                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+|:-------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Solution or Action | <ul><li>'The women of the community decided that they would not make the electricity bill and would send their children to school'</li><li>'Since the children did not have a father the mother decided to enroll them in school'</li><li>'Preeti Kumari age 18 does not want to appear for the matriculation examination due to failure Explain the solution to her parents and try to get her to appear for the examination and take her forward'</li></ul> |
+| Challenge          | <ul><li>'Girls have work responsibilities so they dont get a chance to study'</li><li>'Due to poverty children are unable to pursue further studies'</li><li>'Kavita didis daughter has left her studies after the fifth lesson'</li></ul>                                                                                                                                                                                                                    |
+| Other              | <ul><li>'The community woman shared that one childs birth certificate has not been made which could potentially affect their education'</li><li>'No community members should hold meetings with teachers at school'</li><li>'Regarding Siddhanta it was said that you must study only then your future will be bright and you will not face any problems in future'</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("Education is very important for children in poverty and can also create a future")
+```
+
+<!--
+### 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   | 19.8416 | 402 |
+
+| Label              | Training Sample Count |
+|:-------------------|:----------------------|
+| Challenge          | 1634                  |
+| Other              | 440                   |
+| Solution or Action | 1852                  |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- 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    | 0.2792        | -               |
+| 0.0102 | 50   | 0.2964        | -               |
+| 0.0204 | 100  | 0.2595        | -               |
+| 0.0306 | 150  | 0.23          | -               |
+| 0.0407 | 200  | 0.1881        | -               |
+| 0.0509 | 250  | 0.1349        | -               |
+| 0.0611 | 300  | 0.109         | -               |
+| 0.0713 | 350  | 0.1024        | -               |
+| 0.0815 | 400  | 0.0855        | -               |
+| 0.0917 | 450  | 0.0751        | -               |
+| 0.1019 | 500  | 0.0792        | -               |
+| 0.1121 | 550  | 0.074         | -               |
+| 0.1222 | 600  | 0.0643        | -               |
+| 0.1324 | 650  | 0.0668        | -               |
+| 0.1426 | 700  | 0.0624        | -               |
+| 0.1528 | 750  | 0.0604        | -               |
+| 0.1630 | 800  | 0.0513        | -               |
+| 0.1732 | 850  | 0.0455        | -               |
+| 0.1834 | 900  | 0.038         | -               |
+| 0.1936 | 950  | 0.0439        | -               |
+| 0.2037 | 1000 | 0.0331        | -               |
+| 0.2139 | 1050 | 0.0323        | -               |
+| 0.2241 | 1100 | 0.0269        | -               |
+| 0.2343 | 1150 | 0.0376        | -               |
+| 0.2445 | 1200 | 0.0256        | -               |
+| 0.2547 | 1250 | 0.0279        | -               |
+| 0.2649 | 1300 | 0.0276        | -               |
+| 0.2751 | 1350 | 0.0289        | -               |
+| 0.2852 | 1400 | 0.0241        | -               |
+| 0.2954 | 1450 | 0.0179        | -               |
+| 0.3056 | 1500 | 0.0177        | -               |
+| 0.3158 | 1550 | 0.0226        | -               |
+| 0.3260 | 1600 | 0.0176        | -               |
+| 0.3362 | 1650 | 0.0182        | -               |
+| 0.3464 | 1700 | 0.0171        | -               |
+| 0.3566 | 1750 | 0.0175        | -               |
+| 0.3667 | 1800 | 0.0141        | -               |
+| 0.3769 | 1850 | 0.0177        | -               |
+| 0.3871 | 1900 | 0.0136        | -               |
+| 0.3973 | 1950 | 0.0127        | -               |
+| 0.4075 | 2000 | 0.0173        | -               |
+| 0.4177 | 2050 | 0.0106        | -               |
+| 0.4279 | 2100 | 0.01          | -               |
+| 0.4381 | 2150 | 0.0113        | -               |
+| 0.4482 | 2200 | 0.0091        | -               |
+| 0.4584 | 2250 | 0.0071        | -               |
+| 0.4686 | 2300 | 0.01          | -               |
+| 0.4788 | 2350 | 0.0112        | -               |
+| 0.4890 | 2400 | 0.0104        | -               |
+| 0.4992 | 2450 | 0.0089        | -               |
+| 0.5094 | 2500 | 0.0096        | -               |
+| 0.5196 | 2550 | 0.0098        | -               |
+| 0.5297 | 2600 | 0.0113        | -               |
+| 0.5399 | 2650 | 0.005         | -               |
+| 0.5501 | 2700 | 0.0074        | -               |
+| 0.5603 | 2750 | 0.0078        | -               |
+| 0.5705 | 2800 | 0.007         | -               |
+| 0.5807 | 2850 | 0.0047        | -               |
+| 0.5909 | 2900 | 0.0111        | -               |
+| 0.6011 | 2950 | 0.0054        | -               |
+| 0.6112 | 3000 | 0.0088        | -               |
+| 0.6214 | 3050 | 0.0045        | -               |
+| 0.6316 | 3100 | 0.0031        | -               |
+| 0.6418 | 3150 | 0.0084        | -               |
+| 0.6520 | 3200 | 0.005         | -               |
+| 0.6622 | 3250 | 0.0058        | -               |
+| 0.6724 | 3300 | 0.0048        | -               |
+| 0.6826 | 3350 | 0.0057        | -               |
+| 0.6927 | 3400 | 0.0048        | -               |
+| 0.7029 | 3450 | 0.0059        | -               |
+| 0.7131 | 3500 | 0.0078        | -               |
+| 0.7233 | 3550 | 0.0057        | -               |
+| 0.7335 | 3600 | 0.0056        | -               |
+| 0.7437 | 3650 | 0.0039        | -               |
+| 0.7539 | 3700 | 0.0059        | -               |
+| 0.7641 | 3750 | 0.0058        | -               |
+| 0.7742 | 3800 | 0.0022        | -               |
+| 0.7844 | 3850 | 0.0064        | -               |
+| 0.7946 | 3900 | 0.0033        | -               |
+| 0.8048 | 3950 | 0.0037        | -               |
+| 0.8150 | 4000 | 0.003         | -               |
+| 0.8252 | 4050 | 0.0063        | -               |
+| 0.8354 | 4100 | 0.0047        | -               |
+| 0.8456 | 4150 | 0.0056        | -               |
+| 0.8557 | 4200 | 0.0026        | -               |
+| 0.8659 | 4250 | 0.0028        | -               |
+| 0.8761 | 4300 | 0.0066        | -               |
+| 0.8863 | 4350 | 0.0026        | -               |
+| 0.8965 | 4400 | 0.0045        | -               |
+| 0.9067 | 4450 | 0.0052        | -               |
+| 0.9169 | 4500 | 0.0017        | -               |
+| 0.9271 | 4550 | 0.0056        | -               |
+| 0.9372 | 4600 | 0.0043        | -               |
+| 0.9474 | 4650 | 0.0034        | -               |
+| 0.9576 | 4700 | 0.0042        | -               |
+| 0.9678 | 4750 | 0.0031        | -               |
+| 0.9780 | 4800 | 0.004         | -               |
+| 0.9882 | 4850 | 0.0064        | -               |
+| 0.9984 | 4900 | 0.0033        | -               |
+
+### Framework Versions
+- Python: 3.12.13
+- SetFit: 1.1.0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.10.0+cu128
+- Datasets: 3.2.0
+- Tokenizers: 0.20.3
+
+## 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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+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.10.0+cu128"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Challenge",
+    "Other",
+    "Solution or Action"
+  ]
+}

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

sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "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
+  },
+  "mask_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "[PAD]",
+    "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
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
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+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}