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finetuned-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
+}

finetuned-setfit-model/README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+metrics:
+- accuracy
+widget:
+- text: summon the potion
+- text: recall the summoning charm incantation
+- text: enter quietly and investigate
+- text: Enter the realm of fire and face the trials within
+- text: enter the mysterious portal with care
+pipeline_tag: text-classification
+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: 0.9862068965517241
+      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                                                                                                                               |
+|:---------------|:---------------------------------------------------------------------------------------------------------------------------------------|
+| Scouting       | <ul><li>'scout for any enemies'</li><li>'scout the area for any dangers'</li><li>'scouting at night adds an element of risk'</li></ul> |
+| SummoningCharm | <ul><li>'summon the familiar using charm'</li><li>'Use the charm to summon the lost memories'</li><li>'invoke the power'</li></ul>     |
+| Entering       | <ul><li>'Enter the indicated location silently'</li><li>'venture inside the ruins'</li><li>'enter the house'</li></ul>                 |
+| intent         | <ul><li>'user input'</li></ul>                                                                                                         |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9862   |
+
+## 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("summon the potion")
+```
+
+<!--
+### 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   | 2   | 5.5798 | 13  |
+
+| Label          | Training Sample Count |
+|:---------------|:----------------------|
+| Entering       | 434                   |
+| Scouting       | 450                   |
+| SummoningCharm | 419                   |
+| intent         | 1                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 15
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.1105        | -               |
+| 0.0204 | 50   | 0.1166        | -               |
+| 0.0409 | 100  | 0.0285        | -               |
+| 0.0613 | 150  | 0.0129        | -               |
+| 0.0818 | 200  | 0.0044        | -               |
+| 0.1022 | 250  | 0.0032        | -               |
+| 0.1227 | 300  | 0.001         | -               |
+| 0.1431 | 350  | 0.0007        | -               |
+| 0.1636 | 400  | 0.0005        | -               |
+| 0.1840 | 450  | 0.0159        | -               |
+| 0.2045 | 500  | 0.0004        | -               |
+| 0.2249 | 550  | 0.0004        | -               |
+| 0.2454 | 600  | 0.0516        | -               |
+| 0.2658 | 650  | 0.0004        | -               |
+| 0.2863 | 700  | 0.0004        | -               |
+| 0.3067 | 750  | 0.0015        | -               |
+| 0.3272 | 800  | 0.0006        | -               |
+| 0.3476 | 850  | 0.0297        | -               |
+| 0.3681 | 900  | 0.0003        | -               |
+| 0.3885 | 950  | 0.0003        | -               |
+| 0.4090 | 1000 | 0.0003        | -               |
+| 0.4294 | 1050 | 0.0003        | -               |
+| 0.4499 | 1100 | 0.0008        | -               |
+| 0.4703 | 1150 | 0.0006        | -               |
+| 0.4908 | 1200 | 0.0002        | -               |
+| 0.5112 | 1250 | 0.0004        | -               |
+| 0.5317 | 1300 | 0.0002        | -               |
+| 0.5521 | 1350 | 0.0003        | -               |
+| 0.5726 | 1400 | 0.0002        | -               |
+| 0.5930 | 1450 | 0.0003        | -               |
+| 0.6135 | 1500 | 0.0018        | -               |
+| 0.6339 | 1550 | 0.0002        | -               |
+| 0.6544 | 1600 | 0.0002        | -               |
+| 0.6748 | 1650 | 0.0002        | -               |
+| 0.6953 | 1700 | 0.0002        | -               |
+| 0.7157 | 1750 | 0.0002        | -               |
+| 0.7362 | 1800 | 0.0002        | -               |
+| 0.7566 | 1850 | 0.0004        | -               |
+| 0.7771 | 1900 | 0.0002        | -               |
+| 0.7975 | 1950 | 0.0001        | -               |
+| 0.8180 | 2000 | 0.0002        | -               |
+| 0.8384 | 2050 | 0.0324        | -               |
+| 0.8589 | 2100 | 0.0018        | -               |
+| 0.8793 | 2150 | 0.0001        | -               |
+| 0.8998 | 2200 | 0.0002        | -               |
+| 0.9202 | 2250 | 0.0002        | -               |
+| 0.9407 | 2300 | 0.0001        | -               |
+| 0.9611 | 2350 | 0.0001        | -               |
+| 0.9816 | 2400 | 0.0002        | -               |
+
+### Framework Versions
+- Python: 3.12.3
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.0
+- Transformers: 4.40.2
+- PyTorch: 2.2.0+cu118
+- Datasets: 2.19.1
+- 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.*
+-->

finetuned-setfit-model/config.json

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+{
+  "_name_or_path": "sentence-transformers/paraphrase-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": "4.40.2",
+  "vocab_size": 30527
+}

finetuned-setfit-model/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

finetuned-setfit-model/config_setfit.json

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+{
+  "labels": [
+    "Entering",
+    "Scouting",
+    "SummoningCharm",
+    "intent"
+  ],
+  "normalize_embeddings": false
+}

finetuned-setfit-model/model.safetensors

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finetuned-setfit-model/model_head.pkl

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finetuned-setfit-model/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"
+  }
+]

finetuned-setfit-model/sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

finetuned-setfit-model/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

finetuned-setfit-model/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
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+  },
+  "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>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}