Add SetFit model

.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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

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

@@ -0,0 +1,194 @@
+---
+base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Dominica is striving for multi-sectoral and multi-level adaptation across
+    all segments of society, giving particular consideration to vulnerable groups
+    - the poor, disabled, elderly and Kalinago community; as well as gender disparities.
+    Recognising the threats posed by climate change, Dominica has over the last two
+    decades, undertaken a number of initiatives to respond to this threat. The adaptation
+    component has been revised to incorporate updated information on regional climate
+    change projections and impacts on Caribbean SIDS.
+- text: They live in geographical regions and ecosystems that are the most vulnerable
+    to climate change. These include polar regions, humid tropical forests, high mountains,
+    small islands, coastal regions, and arid and semi-arid lands, among others. The
+    impacts of climate change in such regions have strong implications for the ecosystem-based
+    livelihoods on which many indigenous peoples depend. Moreover, in some regions
+    such as the Pacific, the very existence of many indigenous territories is under
+    threat from rising sea levels that not only pose a grave threat to indigenous
+    peoples’ livelihoods but also to their cultures and ways of life.
+- text: Seek to increase urban resilience by developing master plans for rainwater
+    drainage, improving and extending drainage infrastructure, and implementing flood
+    management systems in vulnerable areas. Adaptive capacity of agro- silvo- pastoral
+    production and promotion of blue economy.
+- text: As the average annual precipitation across the country is expected to decline
+    2.6-3.4% by 2025 and 5.9-6.3% by 2050 this will result direct yield response.
+    As described by PACE experiment59 on the Pastures and Climate Extremes using a
+    factorial combination of elevated temperature (ambient +3°C) and winter/spring
+    extreme drought (60% rainfall reduction) resulted in productivity declines of
+    up to 73%. Functional group identity was not an important predictor of yield response
+    to drought.
+- text: Poor rural households in marginal territories that have a low productive potential
+    and/or that are far from markets and infrastructure are highly vulnerable to climate-change
+    impacts and could easily fall into poverty-environment traps 9. This means that
+    communities that are already struggling economically and geographically isolated
+    are at greater risk of experiencing the negative impacts of climate change on
+    their agricultural livelihoods.
+inference: false
+---
+
+# 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 18 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)
+
+## 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("leavoigt/vulnerability_multilabel_v2")
+# Run inference
+preds = model("Seek to increase urban resilience by developing master plans for rainwater drainage, improving and extending drainage infrastructure, and implementing flood management systems in vulnerable areas. Adaptive capacity of agro- silvo- pastoral production and promotion of blue economy.")
+```
+
+<!--
+### 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   | 61.3809 | 164 |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 0)
+- max_steps: -1
+- sampling_strategy: undersampling
+- 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: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0002 | 1    | 0.25          | -               |
+| 0.2084 | 1000 | 0.0461        | 0.1223          |
+| 0.4168 | 2000 | 0.0169        | 0.1294          |
+| 0.6251 | 3000 | 0.032         | 0.121           |
+| 0.8335 | 4000 | 0.023         | 0.1172          |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.36.2
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.10.0
+- Tokenizers: 0.15.2
+
+## 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 +1,29 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
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+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 12,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.36.2",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,23 @@
+{
+  "normalize_embeddings": true,
+  "labels": [
+    "Agricultural communities",
+    "Children",
+    "Coastal communities",
+    "Ethnic, racial or other minorities",
+    "Fishery communities",
+    "Informal sector workers",
+    "Members of indigenous and local communities",
+    "Migrants and displaced persons",
+    "Older persons",
+    "Other",
+    "Persons living in poverty",
+    "Persons with disabilities",
+    "Persons with pre-existing health conditions",
+    "Residents of drought-prone regions",
+    "Rural populations",
+    "Sexual minorities (LGBTQI+)",
+    "Urban populations",
+    "Women and other genders"
+  ]
+}

model.safetensors

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model_head.pkl

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modules.json

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+    "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": 128,
+  "do_lower_case": false
+}

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special_tokens_map.json

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tokenizer_config.json

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+}