Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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: Is a residential portion of a building that sells alcohol considered "licensed
+    premises" in indiana
+- text: In Michigan, what criteria do courts consider in granting grandparent visitation
+    rights?
+- text: Ohio aggravated arson cases
+- text: In Texas, what protections exist for whistleblowers?
+- text: What did you have for breakfast?
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: nomic-ai/nomic-embed-text-v1.5
+---
+
+# SetFit with nomic-ai/nomic-embed-text-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Classes:** 7 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                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+|:------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Term of Art Interpretations & Application | <ul><li>'How do courts in Illinois define "constructive eviction"?'</li><li>'How do Pennsylvania courts define "reasonable suspicion" in DUI cases?'</li><li>'definition of ex parte'</li></ul>                                                                                                                                                                                                                                            |
+| Out of Scope                              | <ul><li>'Has Capt. Ashley Heiberger ever testified as an expert witness?'</li><li>'Have you recently attended any weddings or special celebrations?'</li><li>'Have you seen any good movies lately?'</li></ul>                                                                                                                                                                                                                             |
+| SDR                                       | <ul><li>'Gonzalez et al. v. Mexico'</li><li>'2021 U.S. Dist. LEXIS 14890'</li><li>'Elizabeth Holmes Theranos ORDER DENYING MOTION FOR RELEASE PENDING APPEAL'</li></ul>                                                                                                                                                                                                                                                                    |
+| Identify Current Law                      | <ul><li>'Does Michigan have a statute of repose?'</li><li>'Mississippi law concerning challenges to changes made in updated HOA regulations'</li><li>'cases on nurse liability for making medication dosage mistake in kentucky'</li></ul>                                                                                                                                                                                                 |
+| Agent decision                            | <ul><li>'Search for  USPTO Patent Decisions: BPAI and PTAB discussing  the integration of a judicial exception into practical applications'</li><li>'Are there any EPA Environmental Appeals Board Decisions regarding the guidelines for establishing a "critical habitat" for wildlife?'</li><li>'Find Merit Systems Protection Board decisions regarding when the plain language of a statute must be treated as controlling'</li></ul> |
+| Q&A - Complex                             | <ul><li>'Are bloodhounds considered reliable for establishing probable cause in Idaho?'</li><li>'What are the requirements to file a class action lawsuit in Florida?'</li><li>'Can a corporation be held liable for damages caused by an employee driving under the influence of alcohol in New York?'</li></ul>                                                                                                                          |
+| Practical Guidance                        | <ul><li>'What does an "Election of Remedy" clause involve in an indemnity agreement? T'</li><li>'Where is Private Company Corporate Governance Board Resolutions Resource Kit T'</li><li>'If I start a law firm in Michigan, what types of employee leave do I need to provide compared to my current firm in Ohio? T'</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("tonyshaw/setfit_pg_70h_nomic-v1.5")
+# Run inference
+preds = model("Ohio aggravated arson cases")
+```
+
+<!--
+### 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   | 11.2193 | 98  |
+
+| Label                                     | Training Sample Count |
+|:------------------------------------------|:----------------------|
+| Agent decision                            | 130                   |
+| Identify Current Law                      | 500                   |
+| Out of Scope                              | 100                   |
+| Practical Guidance                        | 41                    |
+| Q&A - Complex                             | 500                   |
+| SDR                                       | 500                   |
+| Term of Art Interpretations & Application | 500                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.2703        | -               |
+| 0.0176 | 50   | 0.2289        | -               |
+| 0.0352 | 100  | 0.2032        | -               |
+| 0.0528 | 150  | 0.0951        | -               |
+| 0.0704 | 200  | 0.0434        | -               |
+| 0.0881 | 250  | 0.026         | -               |
+| 0.1057 | 300  | 0.0299        | -               |
+| 0.1233 | 350  | 0.02          | -               |
+| 0.1409 | 400  | 0.0136        | -               |
+| 0.1585 | 450  | 0.013         | -               |
+| 0.1761 | 500  | 0.0147        | -               |
+| 0.1937 | 550  | 0.0144        | -               |
+| 0.2113 | 600  | 0.0052        | -               |
+| 0.2290 | 650  | 0.0067        | -               |
+| 0.2466 | 700  | 0.0021        | -               |
+| 0.2642 | 750  | 0.0038        | -               |
+| 0.2818 | 800  | 0.006         | -               |
+| 0.2994 | 850  | 0.0039        | -               |
+| 0.3170 | 900  | 0.0007        | -               |
+| 0.3346 | 950  | 0.0003        | -               |
+| 0.3522 | 1000 | 0.0002        | -               |
+| 0.3698 | 1050 | 0.0026        | -               |
+| 0.3875 | 1100 | 0.0027        | -               |
+| 0.4051 | 1150 | 0.0003        | -               |
+| 0.4227 | 1200 | 0.0012        | -               |
+| 0.4403 | 1250 | 0.0022        | -               |
+| 0.4579 | 1300 | 0.0027        | -               |
+| 0.4755 | 1350 | 0.0014        | -               |
+| 0.4931 | 1400 | 0.0008        | -               |
+| 0.5107 | 1450 | 0.0001        | -               |
+| 0.5284 | 1500 | 0.0013        | -               |
+| 0.5460 | 1550 | 0.0001        | -               |
+| 0.5636 | 1600 | 0.0011        | -               |
+| 0.5812 | 1650 | 0.0           | -               |
+| 0.5988 | 1700 | 0.001         | -               |
+| 0.6164 | 1750 | 0.0001        | -               |
+| 0.6340 | 1800 | 0.0002        | -               |
+| 0.6516 | 1850 | 0.0           | -               |
+| 0.6692 | 1900 | 0.0           | -               |
+| 0.6869 | 1950 | 0.0           | -               |
+| 0.7045 | 2000 | 0.0           | -               |
+| 0.7221 | 2050 | 0.0           | -               |
+| 0.7397 | 2100 | 0.0           | -               |
+| 0.7573 | 2150 | 0.0           | -               |
+| 0.7749 | 2200 | 0.0           | -               |
+| 0.7925 | 2250 | 0.001         | -               |
+| 0.8101 | 2300 | 0.0           | -               |
+| 0.8278 | 2350 | 0.0           | -               |
+| 0.8454 | 2400 | 0.0013        | -               |
+| 0.8630 | 2450 | 0.0           | -               |
+| 0.8806 | 2500 | 0.0001        | -               |
+| 0.8982 | 2550 | 0.0004        | -               |
+| 0.9158 | 2600 | 0.0           | -               |
+| 0.9334 | 2650 | 0.0001        | -               |
+| 0.9510 | 2700 | 0.0           | -               |
+| 0.9687 | 2750 | 0.0           | -               |
+| 0.9863 | 2800 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.1
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.4.1
+- 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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+{
+  "_name_or_path": "nomic-ai/nomic-embed-text-v1.5",
+  "activation_function": "swiglu",
+  "architectures": [
+    "NomicBertModel"
+  ],
+  "attn_pdrop": 0.0,
+  "auto_map": {
+    "AutoConfig": "nomic-ai/nomic-bert-2048--configuration_hf_nomic_bert.NomicBertConfig",
+    "AutoModel": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertModel",
+    "AutoModelForMaskedLM": "nomic-ai/nomic-bert-2048--modeling_hf_nomic_bert.NomicBertForPreTraining"
+  },
+  "bos_token_id": null,
+  "causal": false,
+  "dense_seq_output": true,
+  "embd_pdrop": 0.0,
+  "eos_token_id": null,
+  "fused_bias_fc": true,
+  "fused_dropout_add_ln": true,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-12,
+  "max_trained_positions": 2048,
+  "mlp_fc1_bias": false,
+  "mlp_fc2_bias": false,
+  "model_type": "nomic_bert",
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 8192,
+  "pad_vocab_size_multiple": 64,
+  "parallel_block": false,
+  "parallel_block_tied_norm": false,
+  "prenorm": false,
+  "qkv_proj_bias": false,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "rotary_emb_base": 1000,
+  "rotary_emb_fraction": 1.0,
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+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.0,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "use_flash_attn": true,
+  "use_rms_norm": false,
+  "use_xentropy": true,
+  "vocab_size": 30528
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": [
+    "Agent decision",
+    "Identify Current Law",
+    "Out of Scope",
+    "Practical Guidance",
+    "Q&A - Complex",
+    "SDR",
+    "Term of Art Interpretations & Application"
+  ],
+  "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": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+      "lstrip": false,
+      "normalized": false,
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+}