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--- |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: I'm encountering errors with a pod in the "kube-public" namespace. Any suggestions |
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on how to debug it? |
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- text: Can you check sandbox-1 for problems? |
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- text: I need permissions for the prod-aws account to troubleshoot an issue. |
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- text: Can you tell me about your hobbies? |
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- text: How can I reduce stress? |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.9961538461538462 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 5 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| NONE | <ul><li>'How do I learn to play the guitar?'</li><li>"What's the longest river in the world?"</li><li>'How do I overcome procrastination?'</li></ul> | |
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| KUBIE | <ul><li>'What logs should I check to identify container crashes in the qa-soc-svcs namespace?'</li><li>'Can you suggest ways to troubleshoot an image pull error in the "kube-public" namespace?'</li><li>"I'm encountering errors with a pod in the sandbox-6 namespace. Any suggestions on how to debug it?"</li></ul> | |
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| aws_iam | <ul><li>'Show me the IAM role details including attached policies.'</li><li>'Show me the IAM roles that have the "admin" prefix.'</li><li>'How can I get detailed information about a particular IAM role?'</li></ul> | |
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| DOC | <ul><li>'How to access ArgoCD on Production?'</li><li>'How to run terraform in CDO?'</li><li>'How to push images to dockerhub.cisco.com?'</li></ul> | |
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| access_management | <ul><li>'Access to prod-aws infrastructure is required urgently for a deployment.'</li><li>'Could you provide me access to the dev-aws resources?'</li><li>'I require access to the prod-sagemaker instance for machine learning experiments.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.9962 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("setfit_model_id") |
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# Run inference |
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preds = model("How can I reduce stress?") |
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``` |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 1 | 8.5408 | 17 | |
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| Label | Training Sample Count | |
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|:------------------|:----------------------| |
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| aws_iam | 20 | |
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| access_management | 20 | |
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| DOC | 18 | |
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| KUBIE | 20 | |
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| NONE | 20 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-------:|:-------:|:-------------:|:---------------:| |
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| 0.0021 | 1 | 0.2675 | - | |
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| 0.1042 | 50 | 0.1143 | - | |
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| 0.2083 | 100 | 0.0578 | - | |
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| 0.3125 | 150 | 0.0028 | - | |
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| 0.4167 | 200 | 0.0032 | - | |
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| 0.5208 | 250 | 0.0007 | - | |
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| 0.625 | 300 | 0.0006 | - | |
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| 0.7292 | 350 | 0.0004 | - | |
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| 0.8333 | 400 | 0.0005 | - | |
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| 0.9375 | 450 | 0.0006 | - | |
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| **1.0** | **480** | **-** | **0.0027** | |
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| 1.0417 | 500 | 0.0004 | - | |
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| 1.1458 | 550 | 0.0002 | - | |
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| 1.25 | 600 | 0.0003 | - | |
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| 1.3542 | 650 | 0.0002 | - | |
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| 1.4583 | 700 | 0.0002 | - | |
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| 1.5625 | 750 | 0.0002 | - | |
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| 1.6667 | 800 | 0.0002 | - | |
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| 1.7708 | 850 | 0.0002 | - | |
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| 1.875 | 900 | 0.0002 | - | |
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| 1.9792 | 950 | 0.0001 | - | |
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| 2.0 | 960 | - | 0.0032 | |
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| 2.0833 | 1000 | 0.0001 | - | |
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| 2.1875 | 1050 | 0.0002 | - | |
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| 2.2917 | 1100 | 0.0001 | - | |
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| 2.3958 | 1150 | 0.0002 | - | |
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| 2.5 | 1200 | 0.0002 | - | |
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| 2.6042 | 1250 | 0.0001 | - | |
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| 2.7083 | 1300 | 0.0002 | - | |
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| 2.8125 | 1350 | 0.0001 | - | |
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| 2.9167 | 1400 | 0.0001 | - | |
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| 3.0 | 1440 | - | 0.004 | |
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| 3.0208 | 1450 | 0.0001 | - | |
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| 3.125 | 1500 | 0.0001 | - | |
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| 3.2292 | 1550 | 0.0002 | - | |
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| 3.3333 | 1600 | 0.0002 | - | |
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| 3.4375 | 1650 | 0.0001 | - | |
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| 3.5417 | 1700 | 0.0002 | - | |
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| 3.6458 | 1750 | 0.0001 | - | |
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| 3.75 | 1800 | 0.0001 | - | |
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| 3.8542 | 1850 | 0.0001 | - | |
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| 3.9583 | 1900 | 0.0002 | - | |
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| 4.0 | 1920 | - | 0.0037 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.9.6 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.40.1 |
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- PyTorch: 2.1.2 |
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- Datasets: 2.19.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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