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,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: (1) The purpose of these rules is to provide administrative procedures for
+    fetal, infant, and maternal death reviews, and maternal and family interviews,
+    or both. (2) The program brings together key members of the community to review
+    cases of fetal, infant, and maternal deaths in order to identify the factors associated
+    with those deaths, to determine if those deaths represent system issues that require
+    change, to develop recommendations for change, and to assist in the implementation
+    of change. (3) The program's goal is to enhance the health and well-being of women,
+    infants, and families by improving the community resources and service delivery
+    systems available to them. The programs are operated under the auspices of the
+    Alabama Department of Public Health (ADPH), Bureau of Family Health Services,
+    State Perinatal Program.
+- text: '(a) The enterprise fund may be used to cover closure costs only for major
+    waste tire facilities operated by government agencies. (b) The enterprise fund
+    shall dedicate its revenue exclusively or with exclusive first priority to financing
+    closure activities. (c) The enterprise fund shall be established and the documents
+    shall be worded as specified by using form CalRecycle 144 "Enterprise Fund for
+    Financial Assurances" (03/17), which is incorporated herein by reference. (See
+    Appendix A.) The wording, however, may be modified to accommodate special circumstances
+    on a case-by-case basis, as approved by the Board or its designee. (d) Revenue
+    generated by an enterprise fund shall be deposited into a financial assurance
+    mechanism which: (1) Provides equivalent protection to a trust fund as described
+    in section 18474 of this Article; (2) Shall be funded within five years as described
+    in Section 18474 of this Article; (3) Is used exclusively to finance closure activities
+    and shall remain inviolate against all other claims, including any claims by the
+    operator, the operator''s governing body, and the creditors of the operator and
+    its governing body; (4) Authorizes the Board or its designee to direct the provider
+    of financial assurance to pay closure costs if the Board or its designee determines
+    that the operator has failed to perform closure activities covered by the mechanism;
+    (5) Is maintained by a provider whose financial operations are regulated by a
+    federal or state agency, or the provider is otherwise certain to maintain and
+    disburse the assured funds properly; (6) Is maintained by a provider who has authority
+    to invest revenue deposited into the mechanism. (7) Meets other requirements that
+    the Board determines are necessary to ensure that the assured amount of funds
+    shall be available for closure activities in a timely manner.'
+- text: (a) Various laws provide for the issuance of certifications by the state board
+    or regional boards. These regulations specify how the state board and the regional
+    boards implement various certification programs and how the state board acts on
+    petitions for reconsideration of certification actions or failures to act by the
+    executive director, regional boards, and executive officers. (b) Within five years
+    from the effective date of these regulations, the state board, in consultation
+    with the Secretary for Environmental Protection, shall review the provisions of
+    this Chapter to determine whether they should be retained, revised, or repealed.
+- text: The Tax Reform Act of 1986, as amended, (the "act") establishes a Federal
+    tax credit ("low- income housing credit," "LIHTC" or "credit") administered by
+    state housing agencies for owners of housing for persons of low-income. The act
+    authorizes the governor of each state to allocate the low-income housing credit
+    ceiling among governmental units and other issuing authorities in the state. The
+    act requires that the allocation of credit to owners of low-income housing be
+    coordinated by a single state housing credit agency. The act further requires
+    each agency allocating credits to adopt a qualified allocation plan (the "plan"
+    or the "QAP") which sets forth the criteria and preferences by which credit will
+    be allocated to projects. By Executive Order, the New York State Division of Housing
+    and Community Renewal has been designated as the State Housing Credit Agency to
+    allocate the credit in a manner which maximizes the public benefit by addressing
+    the State's need for low-income housing and community revitalization incentives.
+    In order to provide for the effective coordination of the State's low-income housing
+    credit program with section 42 of the United States Internal Revenue Code (the
+    "code"), this plan shall be construed and administered in a manner consistent
+    with the code and regulations promulgated thereunder.
+- text: It is the purpose and intent of the stream quality objectives specified in
+    the comprehensive plan to apply to artificial (man-made, as opposed to natural)
+    causes of pollution.
+inference: true
+---
+
+# SetFit
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **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:** 32 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("rkoh/setfit-bert-a6-8per")
+# Run inference
+preds = model("It is the purpose and intent of the stream quality objectives specified in the comprehensive plan to apply to artificial (man-made, as opposed to natural) causes of pollution.")
+```
+
+<!--
+### 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   | tensor(31) | tensor(329.9688) | tensor(4265) |
+
+| Label                  | Training Sample Count |
+|:-----------------------|:----------------------|
+| non-purpose            | 0                     |
+| purpose-administrative | 0                     |
+| purpose-regulatory     | 0                     |
+| purpose-with-authority | 0                     |
+| purpose-with-scope     | 0                     |
+
+### 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: True
+
+### Training Results
+| Epoch | Step | Training Loss | Validation Loss |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.025 | 1    | 0.478         | -               |
+| 0.25  | 10   | 0.3818        | -               |
+| 0.5   | 20   | 0.3011        | -               |
+| 0.75  | 30   | 0.2555        | -               |
+| 1.0   | 40   | 0.1938        | 0.2208          |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## 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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+{
+  "_name_or_path": "/content/drive/My Drive/Fall-2024/models/legal-bert",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_ids": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "non-purpose",
+    "purpose-administrative",
+    "purpose-regulatory",
+    "purpose-with-authority",
+    "purpose-with-scope"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5ee76df3676687c9122b7f166b4aee383591e6064f2fa7112dbfa691b93cf29
+size 437951328

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:88f91083fb9a00a715839bd573ca356b3804948275ba10ddc2102890d1cc96c2
+size 31647

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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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "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,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "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]"
+}