Push model using huggingface_hub.

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
+}

README.md

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: WHO and UNICEF has recommended that a child should receive the minimum dietary
+    diversity (MDD) of foods and beverages from at least five out of eight defined
+    food groups to maintain proper growth and development during this critical period
+    19 . In Timor-Leste, 35.3% received minimum dietary diversity (MDD) 4 . On the
+    other hand, the proportion of children 6-23 months receiving MDD has been on the
+    upward rise (28% in 2013 to 35.3% in 2020) although it is still low. Food group
+    diversity is associated with improved linear growth in young children20 . A diet
+    lacking in diversity can increase the risk of micronutrient deficiencies, which
+    may have a damaging effect on 47.0% 81.7% 93.4% 75.2% 30.7% 57.5% 62.3% 50.2%
+    0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% TLDHS 2003 TLDHS 2010 TLFNS 2013 TLFNS
+    2016 46.8% 64.2% TLFNS 2020 Early Initiation (1 hour) Exclusive breastfeeding
+    (0-5 months) 20NATIONAL HEALTH SECTOR NUTRITION STRATEGIC PLAN 2022-2026 children’s
+    physical and cognitive development21 . Consequently, TLFNS 2020 reported that
+    a very high proportion of children 6-23 months had consumed grains, roots, and
+    tubers (97.5%) and breast milk (90.6%), as well as vitamin A-rich fruits and vegetables
+    (71.5%). Consumption of dairy products (0.8%) was low, while consumption of flesh
+    foods (23.1%) and legumes or nuts (31.0%) was also relatively low. The 2020 survey
+    reported that 19.1% of children 6-23 months consumed sugar sweetened beverages,
+    31.0% consumed sweet or savoury junk foods, while 20.0% did not consume any fruits
+    or vegetables and 35.9% consumed no eggs or flesh foods.
+- text: Climate Risk and Vulnerability Baseline. One of the key roles of the NAP process
+    is to develop a common evidence base on CC that can be referenced by stakeholders
+    in various documents, including strategies and project proposals. Therefore, climate
+    risk and vulnerability assessments shall be summarized and updated on a periodical
+    basis to underlie the development of the NAP and the list of m
+- text: 'Agriculture in Armenia has always been remarkable with the high level of
+    climate risks (hail damage, frost damage, drought, etc.). As it is already mentioned,
+    agriculture has suffered losses from natural disasters worth of AMD 110 billion
+    during the recent 6 years. Climate risks in Armenia are a serious problem since
+    there are no clearly formed such state, political or institutional mechanisms,
+    the application of which would make it possible to noticeably mitigate the existing
+    risks. Due to the lack of such mechanisms, the mechanism of full assessment of
+    the agricultural losses does not work too, as well as the risks are not assessed
+    in advance. In this context, to reduce the agricultural risks, to introduce loss
+    compensation mechanisms in a systemized way, and to provide sustainable income
+    levels for economic entities, it is necessary to address the critical issue of
+    agricultural risk insurance. '
+- text: 'Strategy 6.3: Strengthen monitoring, evaluation and surveillance systems
+    for routine information sharing and data utilization at all levels Activities
+    Stakeholder Conduct bi-annual nutrition M&E coordination meetings. ND, M&ED, INS
+    Collaborate with HIS Department (HISD) and M&E Department MOH to conduct routine
+    nutrition data quality assessments and audits (RDQA). ND, HISD, M&ED, INS In collaboration
+    with HISD MOH and M&E Department, train M&E officers, DPHO nutrition, nutrition
+    focal points and Municipality Health Services on data management (collection analyses,
+    interpreting and reporting) at all levels. ND, HISD, M&ED, INS Develop and disseminate
+    the Nutrition M&E Plan. ND, M&ED Strengthen the nutrition information system within
+    the HMIS by integrating key nutrition indicators and databases. ND, HISD, M&ED
+    Establish and scale up a nutrition surveillance system for real time monitoring
+    at all levels. ND, M&ED, INS Conduct mid-term and end-term evaluation of the nutrition
+    strategic plan. ND, HISD, M&ED, INS Conduct a food and nutrition survey every
+    5 years. ND, HISD, M&ED, INS Conduct knowledge attitude and practices (KAP) survey
+    on nutrition. ND, HISD, M&ED, HPD, INS Liaise with HMIS to introduce real-time
+    data collection linked to DHIS2. ND, HISD, M&ED Periodic publishing of nutrition
+    bulletin/report ND, HISD, M&ED Develop and regularly review nutrition indicators
+    monitoring and evaluation guideline. ND, HMIS, M&ED, INS '
+- text: Provision 1 - Access to safe nutritious food for all The package will be aimed
+    at ending hunger and all forms of malnutrition and reduce the incidence of non-communicable
+    diseases, enabling all people to be nourished and healthy. This suggests that
+    all people at all times have access to sufficient quantities of affordable and
+    safe foo
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 512 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **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)
+
+## 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("faodl/setfit-paraphrase-mpnet-base-v2-5ClassesDesc-multilabel-augmented")
+# Run inference
+preds = model("Provision 1 - Access to safe nutritious food for all The package will be aimed at ending hunger and all forms of malnutrition and reduce the incidence of non-communicable diseases, enabling all people to be nourished and healthy. This suggests that all people at all times have access to sufficient quantities of affordable and safe foo")
+```
+
+<!--
+### 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   | 6   | 93.5916 | 1014 |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0010 | 1    | 0.3063        | -               |
+| 0.0524 | 50   | 0.2204        | -               |
+| 0.1047 | 100  | 0.1689        | -               |
+| 0.1571 | 150  | 0.1464        | -               |
+| 0.2094 | 200  | 0.1236        | -               |
+| 0.2618 | 250  | 0.1088        | -               |
+| 0.3141 | 300  | 0.0649        | -               |
+| 0.3665 | 350  | 0.0697        | -               |
+| 0.4188 | 400  | 0.0395        | -               |
+| 0.4712 | 450  | 0.052         | -               |
+| 0.5236 | 500  | 0.0263        | -               |
+| 0.5759 | 550  | 0.0376        | -               |
+| 0.6283 | 600  | 0.0307        | -               |
+| 0.6806 | 650  | 0.022         | -               |
+| 0.7330 | 700  | 0.0162        | -               |
+| 0.7853 | 750  | 0.012         | -               |
+| 0.8377 | 800  | 0.0135        | -               |
+| 0.8901 | 850  | 0.0173        | -               |
+| 0.9424 | 900  | 0.0171        | -               |
+| 0.9948 | 950  | 0.0117        | -               |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.1
+- Sentence Transformers: 3.4.1
+- Transformers: 4.50.2
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.5.0
+- 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": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.50.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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

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"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+    "single_word": false
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+  "mask_token": {
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}