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--- |
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library_name: setfit |
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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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metrics: |
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- metric |
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widget: |
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- text: Damn, my condolences to you bro |
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- text: No Friday Im booked all day |
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- text: Im sorry. |
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- text: Hiding in the bush |
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- text: '*"The conservative party is a cult." Says the group that bans words and follows |
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socialism.??*' |
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pipeline_tag: text-classification |
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inference: false |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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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: metric |
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value: 0.7340375623557441 |
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name: Metric |
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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 ClassifierChain 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 ClassifierChain instance |
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- **Maximum Sequence Length:** 512 tokens |
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<!-- - **Number of Classes:** Unknown --> |
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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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## Evaluation |
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### Metrics |
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| Label | Metric | |
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|:--------|:-------| |
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| **all** | 0.7340 | |
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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("CrisisNarratives/setfit-8classes-multi_label") |
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# Run inference |
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preds = model("Im sorry.") |
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``` |
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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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<!-- |
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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 | 25.3789 | 1681 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (3, 3) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 40 |
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- body_learning_rate: (1.752e-05, 1.752e-05) |
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- head_learning_rate: 1.752e-05 |
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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: 30 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0004 | 1 | 0.4024 | - | |
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| 0.0185 | 50 | 0.2502 | - | |
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| 0.0370 | 100 | 0.2222 | - | |
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| 0.0555 | 150 | 0.2279 | - | |
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| 0.0739 | 200 | 0.2556 | - | |
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| 0.0924 | 250 | 0.2444 | - | |
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| 0.1109 | 300 | 0.2441 | - | |
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| 0.1294 | 350 | 0.2538 | - | |
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| 0.1479 | 400 | 0.2245 | - | |
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| 0.1664 | 450 | 0.2111 | - | |
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| 0.1848 | 500 | 0.1554 | - | |
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| 0.2033 | 550 | 0.1361 | - | |
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| 0.2218 | 600 | 0.1712 | - | |
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| 0.2403 | 650 | 0.1506 | - | |
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| 0.2588 | 700 | 0.1175 | - | |
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| 0.2773 | 750 | 0.0695 | - | |
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| 0.2957 | 800 | 0.0916 | - | |
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| 0.3142 | 850 | 0.0884 | - | |
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| 0.3327 | 900 | 0.0412 | - | |
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| 0.3512 | 950 | 0.1189 | - | |
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| 0.3697 | 1000 | 0.0485 | - | |
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| 0.3882 | 1050 | 0.1098 | - | |
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| 0.4067 | 1100 | 0.0303 | - | |
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| 0.4251 | 1150 | 0.0244 | - | |
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| 0.4436 | 1200 | 0.0429 | - | |
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| 0.4621 | 1250 | 0.034 | - | |
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| 0.4806 | 1300 | 0.0725 | - | |
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| 0.4991 | 1350 | 0.0438 | - | |
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| 0.5176 | 1400 | 0.0124 | - | |
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| 0.5360 | 1450 | 0.1603 | - | |
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| 0.5545 | 1500 | 0.1134 | - | |
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| 0.5730 | 1550 | 0.098 | - | |
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| 0.5915 | 1600 | 0.0343 | - | |
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| 0.6100 | 1650 | 0.0354 | - | |
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| 0.6285 | 1700 | 0.0892 | - | |
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| 0.6470 | 1750 | 0.0137 | - | |
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| 0.6654 | 1800 | 0.071 | - | |
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| 0.6839 | 1850 | 0.0317 | - | |
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| 0.7024 | 1900 | 0.0285 | - | |
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| 0.7209 | 1950 | 0.0311 | - | |
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| 0.7394 | 2000 | 0.0755 | - | |
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| 0.7579 | 2050 | 0.09 | - | |
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| 0.7763 | 2100 | 0.0565 | - | |
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| 0.7948 | 2150 | 0.0099 | - | |
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| 0.8133 | 2200 | 0.0236 | - | |
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| 0.8318 | 2250 | 0.0663 | - | |
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| 0.8503 | 2300 | 0.1391 | - | |
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| 0.8688 | 2350 | 0.0176 | - | |
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| 0.8872 | 2400 | 0.0645 | - | |
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| 0.9057 | 2450 | 0.0318 | - | |
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| 0.9242 | 2500 | 0.0186 | - | |
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| 0.9427 | 2550 | 0.0514 | - | |
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| 0.9612 | 2600 | 0.0261 | - | |
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| 0.9797 | 2650 | 0.0535 | - | |
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| 0.9982 | 2700 | 0.018 | - | |
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| 1.0166 | 2750 | 0.0218 | - | |
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| 1.0351 | 2800 | 0.0351 | - | |
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| 1.0536 | 2850 | 0.0704 | - | |
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| 1.0721 | 2900 | 0.0251 | - | |
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| 1.0906 | 2950 | 0.0156 | - | |
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| 1.1091 | 3000 | 0.0821 | - | |
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| 1.1275 | 3050 | 0.0273 | - | |
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| 1.1460 | 3100 | 0.0719 | - | |
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| 1.1645 | 3150 | 0.0496 | - | |
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| 1.1830 | 3200 | 0.0124 | - | |
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| 1.2015 | 3250 | 0.0576 | - | |
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| 1.2200 | 3300 | 0.0453 | - | |
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| 1.2384 | 3350 | 0.0236 | - | |
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| 1.2569 | 3400 | 0.013 | - | |
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| 1.2754 | 3450 | 0.0909 | - | |
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| 1.2939 | 3500 | 0.024 | - | |
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| 1.3124 | 3550 | 0.0264 | - | |
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| 1.3309 | 3600 | 0.0397 | - | |
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| 1.3494 | 3650 | 0.0484 | - | |
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| 1.3678 | 3700 | 0.0301 | - | |
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| 1.3863 | 3750 | 0.0512 | - | |
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| 1.4048 | 3800 | 0.0625 | - | |
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| 1.4233 | 3850 | 0.0583 | - | |
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| 1.4418 | 3900 | 0.0506 | - | |
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| 1.4603 | 3950 | 0.0561 | - | |
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| 1.4787 | 4000 | 0.0295 | - | |
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| 1.4972 | 4050 | 0.1352 | - | |
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| 1.5157 | 4100 | 0.0101 | - | |
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| 1.5342 | 4150 | 0.0221 | - | |
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| 1.5527 | 4200 | 0.057 | - | |
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| 1.5712 | 4250 | 0.0389 | - | |
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| 1.5896 | 4300 | 0.0173 | - | |
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| 1.6081 | 4350 | 0.0605 | - | |
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| 1.6266 | 4400 | 0.0187 | - | |
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| 1.6451 | 4450 | 0.0401 | - | |
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| 1.6636 | 4500 | 0.0571 | - | |
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| 1.6821 | 4550 | 0.0612 | - | |
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| 1.7006 | 4600 | 0.03 | - | |
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| 1.7190 | 4650 | 0.0299 | - | |
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| 1.7375 | 4700 | 0.0583 | - | |
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| 1.7560 | 4750 | 0.0279 | - | |
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| 1.7745 | 4800 | 0.027 | - | |
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| 1.7930 | 4850 | 0.0343 | - | |
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| 1.8115 | 4900 | 0.0634 | - | |
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| 1.8299 | 4950 | 0.0748 | - | |
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| 1.8484 | 5000 | 0.0699 | - | |
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| 1.8669 | 5050 | 0.0678 | - | |
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| 1.8854 | 5100 | 0.0724 | - | |
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| 1.9039 | 5150 | 0.0211 | - | |
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| 1.9224 | 5200 | 0.037 | - | |
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| 1.9409 | 5250 | 0.0891 | - | |
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| 1.9593 | 5300 | 0.0235 | - | |
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| 1.9778 | 5350 | 0.0339 | - | |
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| 1.9963 | 5400 | 0.029 | - | |
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| 2.0148 | 5450 | 0.1292 | - | |
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| 2.0333 | 5500 | 0.0457 | - | |
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| 2.0518 | 5550 | 0.0577 | - | |
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| 2.0702 | 5600 | 0.063 | - | |
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| 2.0887 | 5650 | 0.0198 | - | |
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| 2.1072 | 5700 | 0.0367 | - | |
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| 2.1257 | 5750 | 0.0475 | - | |
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| 2.1442 | 5800 | 0.0368 | - | |
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| 2.1627 | 5850 | 0.0401 | - | |
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| 2.1811 | 5900 | 0.0353 | - | |
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| 2.1996 | 5950 | 0.0387 | - | |
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| 2.2181 | 6000 | 0.0325 | - | |
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| 2.2366 | 6050 | 0.046 | - | |
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| 2.2551 | 6100 | 0.03 | - | |
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| 2.2736 | 6150 | 0.0338 | - | |
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| 2.2921 | 6200 | 0.0374 | - | |
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| 2.3105 | 6250 | 0.0206 | - | |
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| 2.3290 | 6300 | 0.031 | - | |
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| 2.3475 | 6350 | 0.0493 | - | |
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| 2.3660 | 6400 | 0.0182 | - | |
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| 2.3845 | 6450 | 0.0352 | - | |
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| 2.4030 | 6500 | 0.0622 | - | |
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| 2.4214 | 6550 | 0.0682 | - | |
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| 2.4399 | 6600 | 0.0227 | - | |
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| 2.4584 | 6650 | 0.0401 | - | |
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| 2.4769 | 6700 | 0.0348 | - | |
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| 2.4954 | 6750 | 0.0417 | - | |
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| 2.5139 | 6800 | 0.0232 | - | |
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| 2.5323 | 6850 | 0.0603 | - | |
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| 2.5508 | 6900 | 0.0981 | - | |
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| 2.5693 | 6950 | 0.0433 | - | |
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| 2.5878 | 7000 | 0.0187 | - | |
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| 2.6063 | 7050 | 0.0099 | - | |
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| 2.6248 | 7100 | 0.0276 | - | |
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| 2.6433 | 7150 | 0.0516 | - | |
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| 2.6617 | 7200 | 0.0211 | - | |
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| 2.6802 | 7250 | 0.0191 | - | |
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| 2.6987 | 7300 | 0.1152 | - | |
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| 2.7172 | 7350 | 0.0442 | - | |
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| 2.7357 | 7400 | 0.0226 | - | |
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| 2.7542 | 7450 | 0.0429 | - | |
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| 2.7726 | 7500 | 0.0313 | - | |
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| 2.7911 | 7550 | 0.0601 | - | |
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| 2.8096 | 7600 | 0.0156 | - | |
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| 2.8281 | 7650 | 0.039 | - | |
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| 2.8466 | 7700 | 0.0239 | - | |
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| 2.8651 | 7750 | 0.1159 | - | |
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| 2.8835 | 7800 | 0.0223 | - | |
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| 2.9020 | 7850 | 0.0442 | - | |
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| 2.9205 | 7900 | 0.0254 | - | |
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| 2.9390 | 7950 | 0.0268 | - | |
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| 2.9575 | 8000 | 0.0415 | - | |
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| 2.9760 | 8050 | 0.0235 | - | |
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| 2.9945 | 8100 | 0.0177 | - | |
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### Framework Versions |
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- Python: 3.9.16 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.35.0 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.14.6 |
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- Tokenizers: 0.14.1 |
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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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