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--- |
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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: if it is raining, as was stated, then it is irrelevant what someone thinks |
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abut whether or not it is raining. it is raining. therefore, the statement was |
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nonsensical. |
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- text: the first part of the sentence was a fact but the second half was sally's |
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opinion |
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- text: because on one hand it is but actually not a long term solution |
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- text: it contradicted itself |
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- text: cyberbully may seem cruel to everyone, but to tom, he does not feel cruel |
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to him. |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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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: accuracy |
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value: 0.868421052631579 |
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name: Accuracy |
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- type: precision |
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value: 0.5642857142857144 |
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name: Precision |
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- type: recall |
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value: 0.5629370629370629 |
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name: Recall |
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- type: f1 |
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value: 0.562610229276896 |
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name: F1 |
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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:** 3 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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| Linguistic (in)felicity | <ul><li>'because the second statement negates what was stated in the first part of the sentence'</li><li>'there is a logic conflict in the statement that renders it bizarre and nonsensical.'</li><li>'there was a contradiction of statements if read at face value, however, it could be read that being homeless is not right in which case the statement would make sense. it is unclear.'</li></ul> | |
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| Enrichment / reinterpretation | <ul><li>'the statement recognised the objective compassion but the opinion contradicted it'</li><li>"because while it is compassionate to help the homeless people don't always do it out of compassion."</li><li>'it could be the way how homeless are helped. there could be better ways to handle that'</li></ul> | |
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| Lack of understanding / clear misunderstanding | <ul><li>'it simply sounded stupid. i doubt it makes any sense'</li><li>'it statement didnt make any sense, for us to better understand, tom needs to further explain his reason for stating why its not cruel after first saying it is'</li><li>'it sounds very contradictory'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | Precision | Recall | F1 | |
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|:--------|:---------|:----------|:-------|:-------| |
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| **all** | 0.8684 | 0.5643 | 0.5629 | 0.5626 | |
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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("it contradicted itself") |
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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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<!-- |
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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 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 | 2 | 16.6447 | 92 | |
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| Label | Training Sample Count | |
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|:-----------------------------------------------|:----------------------| |
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| Enrichment / reinterpretation | 31 | |
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| Lack of understanding / clear misunderstanding | 10 | |
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| Linguistic (in)felicity | 111 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (10, 10) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-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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- l2_weight: 0.01 |
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- seed: 3786 |
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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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| 0.0026 | 1 | 0.2539 | - | |
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| 0.1316 | 50 | 0.2248 | - | |
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| 0.2632 | 100 | 0.1681 | - | |
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| 0.3947 | 150 | 0.0854 | - | |
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| 0.5263 | 200 | 0.0128 | - | |
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| 0.6579 | 250 | 0.0074 | - | |
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| 0.7895 | 300 | 0.0017 | - | |
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| 0.9211 | 350 | 0.0021 | - | |
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| 1.0526 | 400 | 0.0024 | - | |
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| 1.1842 | 450 | 0.0004 | - | |
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| 1.3158 | 500 | 0.0011 | - | |
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| 1.4474 | 550 | 0.0016 | - | |
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| 1.5789 | 600 | 0.0003 | - | |
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| 1.7105 | 650 | 0.0002 | - | |
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| 1.8421 | 700 | 0.0002 | - | |
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| 1.9737 | 750 | 0.0002 | - | |
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| 2.1053 | 800 | 0.0002 | - | |
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| 2.2368 | 850 | 0.0002 | - | |
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| 2.3684 | 900 | 0.0002 | - | |
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| 2.5 | 950 | 0.0001 | - | |
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| 2.6316 | 1000 | 0.0001 | - | |
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| 2.7632 | 1050 | 0.0001 | - | |
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| 2.8947 | 1100 | 0.0001 | - | |
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| 3.0263 | 1150 | 0.0001 | - | |
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| 3.1579 | 1200 | 0.0001 | - | |
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| 3.2895 | 1250 | 0.0001 | - | |
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| 3.4211 | 1300 | 0.0001 | - | |
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| 3.5526 | 1350 | 0.0001 | - | |
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| 3.6842 | 1400 | 0.0001 | - | |
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| 3.8158 | 1450 | 0.0001 | - | |
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| 3.9474 | 1500 | 0.0001 | - | |
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| 4.0789 | 1550 | 0.0001 | - | |
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| 4.2105 | 1600 | 0.0001 | - | |
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| 4.3421 | 1650 | 0.0001 | - | |
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| 4.4737 | 1700 | 0.0001 | - | |
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| 4.6053 | 1750 | 0.0001 | - | |
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| 4.7368 | 1800 | 0.0001 | - | |
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| 4.8684 | 1850 | 0.0001 | - | |
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| 5.0 | 1900 | 0.0001 | - | |
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| 5.1316 | 1950 | 0.0001 | - | |
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| 5.2632 | 2000 | 0.0001 | - | |
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| 5.3947 | 2050 | 0.0001 | - | |
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| 5.5263 | 2100 | 0.0001 | - | |
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| 5.6579 | 2150 | 0.0001 | - | |
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| 5.7895 | 2200 | 0.0001 | - | |
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| 5.9211 | 2250 | 0.0001 | - | |
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| 6.0526 | 2300 | 0.0001 | - | |
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| 6.1842 | 2350 | 0.0001 | - | |
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| 6.3158 | 2400 | 0.0001 | - | |
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| 6.4474 | 2450 | 0.0001 | - | |
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| 6.5789 | 2500 | 0.0001 | - | |
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| 6.7105 | 2550 | 0.0001 | - | |
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| 6.8421 | 2600 | 0.0001 | - | |
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| 6.9737 | 2650 | 0.0001 | - | |
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| 7.1053 | 2700 | 0.0001 | - | |
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| 7.2368 | 2750 | 0.0001 | - | |
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| 7.3684 | 2800 | 0.0001 | - | |
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| 7.5 | 2850 | 0.0001 | - | |
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| 7.6316 | 2900 | 0.0001 | - | |
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| 7.7632 | 2950 | 0.0001 | - | |
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| 7.8947 | 3000 | 0.0001 | - | |
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| 8.0263 | 3050 | 0.0001 | - | |
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| 8.1579 | 3100 | 0.0001 | - | |
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| 8.2895 | 3150 | 0.0001 | - | |
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| 8.4211 | 3200 | 0.0001 | - | |
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| 8.5526 | 3250 | 0.0001 | - | |
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| 8.6842 | 3300 | 0.0001 | - | |
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| 8.8158 | 3350 | 0.0001 | - | |
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| 8.9474 | 3400 | 0.0012 | - | |
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| 9.0789 | 3450 | 0.0003 | - | |
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| 9.2105 | 3500 | 0.0001 | - | |
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| 9.3421 | 3550 | 0.0001 | - | |
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| 9.4737 | 3600 | 0.0001 | - | |
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| 9.6053 | 3650 | 0.0001 | - | |
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| 9.7368 | 3700 | 0.0001 | - | |
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| 9.8684 | 3750 | 0.0001 | - | |
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| 10.0 | 3800 | 0.0 | - | |
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### Framework Versions |
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- Python: 3.11.9 |
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- SetFit: 1.1.3 |
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- Sentence Transformers: 5.1.0 |
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- Transformers: 4.55.2 |
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- PyTorch: 2.8.0 |
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- Datasets: 4.0.0 |
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- Tokenizers: 0.21.4 |
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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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