Add SetFit model

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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- Precision_micro
+- Precision_weighted
+- Precision_samples
+- Recall_micro
+- Recall_weighted
+- Recall_samples
+- F1-Score
+- accuracy
+widget:
+- text: Violence from intimate partners and male family members can escalate during
+    emergencies. This tends to increase as the crisis worsens, and men have lost their
+    jobs and status – particularly in communities with traditional gender roles, and
+    where family violence is normalised
+- text: Expand livelihood protection policies that assist vulnerable, low-income individuals
+    to recover from damages associated with extreme weather events; provide support
+    and protection for internally displaced persons, persons displaced across borders
+    and host communities;. By 2026, draw up disaster recovery plans for all 22 municipalities
+    with resource inventories, first response measures and actions (including on logistics)
+    concerning humanitarian post-disaster needs.
+- text: recurrent droughts, (decrease in amount of rainfall from 550 to 400mm in the
+    highlands), changes in seasonality that had resulted frequent crop failure, massive
+    death of livestock, genetic erosion, extinction of endemic species, degradation
+    of habitats and disequilibria in the ecosystem structure and function. The impact
+    of climate change is manifested in recurrent droughts, desertification, sea level
+    rise and increase in sea water temperature, depletion of ground water, widespread
+    land degradation, and emergence of climate sensitive diseases.
+- 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: Overcoming Poverty. Colombia, as a developing country, faces major socioeconomic
+    challenges. According to the official figures of DANE, by 2014, the percentage
+    of people in multidimensional poverty situation was 21.9% (this figure rises to
+    44.1% if we take into account only the rural population). For the same year, 28.5%
+    of the population was found in a situation of monetary poverty (41.4% of the population
+    in the case of the villages and rural centers scattered).
+pipeline_tag: text-classification
+inference: false
+base_model: sentence-transformers/all-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: Precision_micro
+      value: 0.7972027972027972
+      name: Precision_Micro
+    - type: Precision_weighted
+      value: 0.8053038510784989
+      name: Precision_Weighted
+    - type: Precision_samples
+      value: 0.7972027972027972
+      name: Precision_Samples
+    - type: Recall_micro
+      value: 0.7972027972027972
+      name: Recall_Micro
+    - type: Recall_weighted
+      value: 0.7972027972027972
+      name: Recall_Weighted
+    - type: Recall_samples
+      value: 0.7972027972027972
+      name: Recall_Samples
+    - type: F1-Score
+      value: 0.7972027972027972
+      name: F1-Score
+    - type: accuracy
+      value: 0.7972027972027972
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 384 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)
+
+## Evaluation
+
+### Metrics
+| Label   | Precision_Micro | Precision_Weighted | Precision_Samples | Recall_Micro | Recall_Weighted | Recall_Samples | F1-Score | Accuracy |
+|:--------|:----------------|:-------------------|:------------------|:-------------|:----------------|:---------------|:---------|:---------|
+| **all** | 0.7972          | 0.8053             | 0.7972            | 0.7972       | 0.7972          | 0.7972         | 0.7972   | 0.7972   |
+
+## 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_target")
+# Run inference
+preds = model("Violence from intimate partners and male family members can escalate during emergencies. This tends to increase as the crisis worsens, and men have lost their jobs and status – particularly in communities with traditional gender roles, and where family violence is normalised")
+```
+
+<!--
+### 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   | 15  | 71.9518 | 238 |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0012 | 1    | 0.2559        | -               |
+| 0.0602 | 50   | 0.2509        | -               |
+| 0.1205 | 100  | 0.2595        | -               |
+| 0.1807 | 150  | 0.0868        | -               |
+| 0.2410 | 200  | 0.0302        | -               |
+| 0.3012 | 250  | 0.0024        | -               |
+| 0.3614 | 300  | 0.0225        | -               |
+| 0.4217 | 350  | 0.0007        | -               |
+| 0.4819 | 400  | 0.0004        | -               |
+| 0.5422 | 450  | 0.0003        | -               |
+| 0.6024 | 500  | 0.0002        | -               |
+| 0.6627 | 550  | 0.0005        | -               |
+| 0.7229 | 600  | 0.0319        | -               |
+| 0.7831 | 650  | 0.0001        | -               |
+| 0.8434 | 700  | 0.0104        | -               |
+| 0.9036 | 750  | 0.0003        | -               |
+| 0.9639 | 800  | 0.0009        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.25.1
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.13.3
+
+## 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": "/root/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
+  "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": "4.25.1",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

config_setfit.json

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

model_head.pkl

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+size 13956

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"
+  }
+]

pytorch_model.bin

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+size 438016938

sentence_bert_config.json

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+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}