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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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+---
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Proof Reader
+- text: product owner
+- text: chief community officer
+- text: planner
+- text: information technology administrator
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# 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 [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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **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:** 4 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                                                                                                       |
+|:------|:---------------------------------------------------------------------------------------------------------------|
+| 3     | <ul><li>'academic head'</li><li>'admin director'</li><li>'admin head'</li></ul>                                |
+| 4     | <ul><li>'account director'</li><li>'area vice president'</li><li>'assistant chief executive officer'</li></ul> |
+| 2     | <ul><li>'account manager'</li><li>'admin'</li><li>'admin officer'</li></ul>                                    |
+| 1     | <ul><li>'accountant'</li><li>'administrator'</li><li>'adviser'</li></ul>                                       |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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("setfit_model_id")
+# Run inference
+preds = model("planner")
+```
+
+<!--
+### 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   | 2.1124 | 6   |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 1     | 380                   |
+| 2     | 107                   |
+| 3     | 67                    |
+| 4     | 193                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0005 | 1    | 0.2621        | -               |
+| 0.0268 | 50   | 0.2631        | -               |
+| 0.0535 | 100  | 0.2043        | -               |
+| 0.0803 | 150  | 0.1561        | -               |
+| 0.1071 | 200  | 0.203         | -               |
+| 0.1338 | 250  | 0.1823        | -               |
+| 0.1606 | 300  | 0.1082        | -               |
+| 0.1874 | 350  | 0.0702        | -               |
+| 0.2141 | 400  | 0.1159        | -               |
+| 0.2409 | 450  | 0.0532        | -               |
+| 0.2677 | 500  | 0.0767        | -               |
+| 0.2944 | 550  | 0.0965        | -               |
+| 0.3212 | 600  | 0.0479        | -               |
+| 0.3480 | 650  | 0.0353        | -               |
+| 0.3747 | 700  | 0.0235        | -               |
+| 0.4015 | 750  | 0.0028        | -               |
+| 0.4283 | 800  | 0.004         | -               |
+| 0.4550 | 850  | 0.0908        | -               |
+| 0.4818 | 900  | 0.0078        | -               |
+| 0.5086 | 950  | 0.0149        | -               |
+| 0.5353 | 1000 | 0.0841        | -               |
+| 0.5621 | 1050 | 0.0141        | -               |
+| 0.5889 | 1100 | 0.0328        | -               |
+| 0.6156 | 1150 | 0.0031        | -               |
+| 0.6424 | 1200 | 0.0027        | -               |
+| 0.6692 | 1250 | 0.0205        | -               |
+| 0.6959 | 1300 | 0.0584        | -               |
+| 0.7227 | 1350 | 0.002         | -               |
+| 0.7495 | 1400 | 0.0009        | -               |
+| 0.7762 | 1450 | 0.0018        | -               |
+| 0.8030 | 1500 | 0.001         | -               |
+| 0.8298 | 1550 | 0.0004        | -               |
+| 0.8565 | 1600 | 0.0008        | -               |
+| 0.8833 | 1650 | 0.0006        | -               |
+| 0.9101 | 1700 | 0.0021        | -               |
+| 0.9368 | 1750 | 0.009         | -               |
+| 0.9636 | 1800 | 0.0031        | -               |
+| 0.9904 | 1850 | 0.0024        | -               |
+| 1.0171 | 1900 | 0.0327        | -               |
+| 1.0439 | 1950 | 0.0257        | -               |
+| 1.0707 | 2000 | 0.0006        | -               |
+| 1.0974 | 2050 | 0.0009        | -               |
+| 1.1242 | 2100 | 0.0006        | -               |
+| 1.1510 | 2150 | 0.0004        | -               |
+| 1.1777 | 2200 | 0.0011        | -               |
+| 1.2045 | 2250 | 0.0004        | -               |
+| 1.2313 | 2300 | 0.0012        | -               |
+| 1.2580 | 2350 | 0.0005        | -               |
+| 1.2848 | 2400 | 0.0013        | -               |
+| 1.3116 | 2450 | 0.0007        | -               |
+| 1.3383 | 2500 | 0.0002        | -               |
+| 1.3651 | 2550 | 0.0005        | -               |
+| 1.3919 | 2600 | 0.0006        | -               |
+| 1.4186 | 2650 | 0.0006        | -               |
+| 1.4454 | 2700 | 0.0004        | -               |
+| 1.4722 | 2750 | 0.0004        | -               |
+| 1.4989 | 2800 | 0.0008        | -               |
+| 1.5257 | 2850 | 0.0003        | -               |
+| 1.5525 | 2900 | 0.0012        | -               |
+| 1.5792 | 2950 | 0.0006        | -               |
+| 1.6060 | 3000 | 0.0003        | -               |
+| 1.6328 | 3050 | 0.0002        | -               |
+| 1.6595 | 3100 | 0.0026        | -               |
+| 1.6863 | 3150 | 0.0003        | -               |
+| 1.7131 | 3200 | 0.0003        | -               |
+| 1.7398 | 3250 | 0.0003        | -               |
+| 1.7666 | 3300 | 0.0003        | -               |
+| 1.7934 | 3350 | 0.0003        | -               |
+| 1.8201 | 3400 | 0.0004        | -               |
+| 1.8469 | 3450 | 0.0003        | -               |
+| 1.8737 | 3500 | 0.0005        | -               |
+| 1.9004 | 3550 | 0.0003        | -               |
+| 1.9272 | 3600 | 0.0003        | -               |
+| 1.9540 | 3650 | 0.0002        | -               |
+| 1.9807 | 3700 | 0.0003        | -               |
+| 2.0075 | 3750 | 0.0003        | -               |
+| 2.0343 | 3800 | 0.0003        | -               |
+| 2.0610 | 3850 | 0.0002        | -               |
+| 2.0878 | 3900 | 0.0004        | -               |
+| 2.1146 | 3950 | 0.0003        | -               |
+| 2.1413 | 4000 | 0.0003        | -               |
+| 2.1681 | 4050 | 0.0002        | -               |
+| 2.1949 | 4100 | 0.0541        | -               |
+| 2.2216 | 4150 | 0.0002        | -               |
+| 2.2484 | 4200 | 0.0003        | -               |
+| 2.2752 | 4250 | 0.0582        | -               |
+| 2.3019 | 4300 | 0.0003        | -               |
+| 2.3287 | 4350 | 0.0002        | -               |
+| 2.3555 | 4400 | 0.0003        | -               |
+| 2.3822 | 4450 | 0.0005        | -               |
+| 2.4090 | 4500 | 0.0004        | -               |
+| 2.4358 | 4550 | 0.0003        | -               |
+| 2.4625 | 4600 | 0.0003        | -               |
+| 2.4893 | 4650 | 0.0002        | -               |
+| 2.5161 | 4700 | 0.0002        | -               |
+| 2.5428 | 4750 | 0.0003        | -               |
+| 2.5696 | 4800 | 0.0008        | -               |
+| 2.5964 | 4850 | 0.0002        | -               |
+| 2.6231 | 4900 | 0.0002        | -               |
+| 2.6499 | 4950 | 0.0005        | -               |
+| 2.6767 | 5000 | 0.0003        | -               |
+| 2.7034 | 5050 | 0.0002        | -               |
+| 2.7302 | 5100 | 0.0004        | -               |
+| 2.7570 | 5150 | 0.0002        | -               |
+| 2.7837 | 5200 | 0.0005        | -               |
+| 2.8105 | 5250 | 0.0004        | -               |
+| 2.8373 | 5300 | 0.0394        | -               |
+| 2.8640 | 5350 | 0.0002        | -               |
+| 2.8908 | 5400 | 0.0399        | -               |
+| 2.9176 | 5450 | 0.0002        | -               |
+| 2.9443 | 5500 | 0.0002        | -               |
+| 2.9711 | 5550 | 0.0002        | -               |
+| 2.9979 | 5600 | 0.0002        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.0
+- PyTorch: 2.3.1+cu121
+- Datasets: 2.20.0
+- 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

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+{
+  "_name_or_path": "./job_level_model",
+  "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": "4.39.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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+size 437967672

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
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "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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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+    },
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+      "normalized": false,
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+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}