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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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- accuracy |
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widget: |
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- text: But the author is Bharath Ganesh. |
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- text: The documents, which suggest all the adults were involved in the training, |
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say a person serving as a foster parent caring for one of the kids revealed the |
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details about the training. |
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- text: Louis Farrakhan, the 84-year-old head of the Nation of Islam, has been back |
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in the headlines after a previously unreleased photo of him with President Barack |
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Obama was published in January and Mr. Farrakhan gave an anti-Semitic speech at |
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his organization’s annual convention last month. |
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- text: The name of that CIA official whose torture activities the Post described |
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is Gina Haspel. |
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- text: This is not just about Facebook or Twitter. |
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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: accuracy |
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value: 0.7125193199381762 |
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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 OneVsRestClassifier 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 OneVsRestClassifier 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 | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.7125 | |
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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("anismahmahi/G2-with-noPropaganda-multilabel-setfit-model") |
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# Run inference |
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preds = model("But the author is Bharath Ganesh.") |
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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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<!-- |
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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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<!-- |
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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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<!-- |
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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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--> |
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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 | 23.3972 | 129 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 10 |
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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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| 0.0003 | 1 | 0.3874 | - | |
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| 0.0135 | 50 | 0.3734 | - | |
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| 0.0270 | 100 | 0.2741 | - | |
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| 0.0405 | 150 | 0.2802 | - | |
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| 0.0539 | 200 | 0.2355 | - | |
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| 0.0674 | 250 | 0.2616 | - | |
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| 0.0809 | 300 | 0.262 | - | |
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| 0.0944 | 350 | 0.2302 | - | |
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| 0.1079 | 400 | 0.1962 | - | |
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| 0.1214 | 450 | 0.1438 | - | |
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| 0.1348 | 500 | 0.2001 | - | |
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| 0.1483 | 550 | 0.2126 | - | |
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| 0.1618 | 600 | 0.1244 | - | |
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| 0.1753 | 650 | 0.1968 | - | |
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| 0.1888 | 700 | 0.1473 | - | |
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| 0.2023 | 750 | 0.2407 | - | |
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| 0.2157 | 800 | 0.1607 | - | |
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| 0.2292 | 850 | 0.1376 | - | |
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| 0.2427 | 900 | 0.145 | - | |
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| 0.2562 | 950 | 0.1439 | - | |
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| 0.2697 | 1000 | 0.0418 | - | |
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| 0.2832 | 1050 | 0.0822 | - | |
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| 0.2967 | 1100 | 0.1042 | - | |
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| 0.3101 | 1150 | 0.0381 | - | |
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| 0.3236 | 1200 | 0.17 | - | |
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| 0.3371 | 1250 | 0.0253 | - | |
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| 0.3506 | 1300 | 0.1009 | - | |
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| 0.3641 | 1350 | 0.1355 | - | |
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| 0.3776 | 1400 | 0.0314 | - | |
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| 0.3910 | 1450 | 0.2185 | - | |
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| 0.4045 | 1500 | 0.0774 | - | |
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| 0.4180 | 1550 | 0.0512 | - | |
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| 0.4315 | 1600 | 0.0814 | - | |
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| 0.4450 | 1650 | 0.0169 | - | |
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| 0.4585 | 1700 | 0.0591 | - | |
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| 0.4720 | 1750 | 0.1232 | - | |
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| 0.4854 | 1800 | 0.0941 | - | |
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| 0.4989 | 1850 | 0.1024 | - | |
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| 0.5124 | 1900 | 0.0031 | - | |
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| 0.5259 | 1950 | 0.037 | - | |
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| 0.5394 | 2000 | 0.1418 | - | |
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| 0.5529 | 2050 | 0.0685 | - | |
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| 0.5663 | 2100 | 0.0326 | - | |
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| 0.5798 | 2150 | 0.0143 | - | |
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| 0.5933 | 2200 | 0.064 | - | |
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| 0.6068 | 2250 | 0.0612 | - | |
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| 0.6203 | 2300 | 0.0689 | - | |
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| 0.6338 | 2350 | 0.1402 | - | |
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| 0.6472 | 2400 | 0.288 | - | |
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| 0.6607 | 2450 | 0.0075 | - | |
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| 0.6742 | 2500 | 0.0785 | - | |
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| 0.6877 | 2550 | 0.0339 | - | |
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| 0.7012 | 2600 | 0.0668 | - | |
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| 0.7147 | 2650 | 0.0319 | - | |
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| 0.7282 | 2700 | 0.0622 | - | |
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| 0.7416 | 2750 | 0.1169 | - | |
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| 0.7551 | 2800 | 0.0249 | - | |
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| 0.7686 | 2850 | 0.0218 | - | |
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| 0.7821 | 2900 | 0.0621 | - | |
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| 0.7956 | 2950 | 0.0698 | - | |
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| 0.8091 | 3000 | 0.0562 | - | |
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| 0.8225 | 3050 | 0.0412 | - | |
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| 0.8360 | 3100 | 0.0048 | - | |
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| 0.8495 | 3150 | 0.0085 | - | |
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| 0.8630 | 3200 | 0.0122 | - | |
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| 0.8765 | 3250 | 0.0387 | - | |
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| 0.8900 | 3300 | 0.0053 | - | |
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| 0.9035 | 3350 | 0.0032 | - | |
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| 0.9169 | 3400 | 0.0156 | - | |
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| 0.9304 | 3450 | 0.0013 | - | |
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| 0.9439 | 3500 | 0.001 | - | |
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| 0.9574 | 3550 | 0.0009 | - | |
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| 0.9709 | 3600 | 0.0025 | - | |
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| 0.9844 | 3650 | 0.0006 | - | |
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| 0.9978 | 3700 | 0.0832 | - | |
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| 1.0 | 3708 | - | 0.2776 | |
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| 1.0113 | 3750 | 0.0735 | - | |
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| 1.0248 | 3800 | 0.0053 | - | |
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| 1.0383 | 3850 | 0.0614 | - | |
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| 1.0518 | 3900 | 0.0005 | - | |
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| 1.0653 | 3950 | 0.0046 | - | |
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| 1.0787 | 4000 | 0.0024 | - | |
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| 1.0922 | 4050 | 0.0004 | - | |
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| 1.1057 | 4100 | 0.0016 | - | |
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| 1.1192 | 4150 | 0.0789 | - | |
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| 1.1327 | 4200 | 0.0016 | - | |
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| 1.1462 | 4250 | 0.0018 | - | |
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| 1.1597 | 4300 | 0.0005 | - | |
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| 1.1731 | 4350 | 0.0051 | - | |
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| 1.1866 | 4400 | 0.0139 | - | |
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| 1.2001 | 4450 | 0.0021 | - | |
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| 1.2136 | 4500 | 0.0064 | - | |
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| 1.2271 | 4550 | 0.0025 | - | |
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| 1.2406 | 4600 | 0.0054 | - | |
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| 1.2540 | 4650 | 0.0022 | - | |
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| 1.2675 | 4700 | 0.0734 | - | |
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| 1.2810 | 4750 | 0.026 | - | |
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| 1.2945 | 4800 | 0.0004 | - | |
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| 1.3080 | 4850 | 0.0574 | - | |
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| 1.3215 | 4900 | 0.0043 | - | |
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| 1.3350 | 4950 | 0.0975 | - | |
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| 1.3484 | 5000 | 0.0125 | - | |
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| 1.3619 | 5050 | 0.0045 | - | |
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| 1.3754 | 5100 | 0.0011 | - | |
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| 1.3889 | 5150 | 0.0061 | - | |
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| 1.4024 | 5200 | 0.0004 | - | |
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| 1.4159 | 5250 | 0.0278 | - | |
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| 1.4293 | 5300 | 0.005 | - | |
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| 1.4428 | 5350 | 0.0302 | - | |
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| 1.4563 | 5400 | 0.0341 | - | |
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| 1.4698 | 5450 | 0.0007 | - | |
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| 1.4833 | 5500 | 0.0128 | - | |
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| 1.4968 | 5550 | 0.0459 | - | |
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| 1.5102 | 5600 | 0.0128 | - | |
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| 1.5237 | 5650 | 0.0003 | - | |
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| 1.5372 | 5700 | 0.004 | - | |
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| 1.5507 | 5750 | 0.0005 | - | |
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| 1.5642 | 5800 | 0.0005 | - | |
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| 1.5777 | 5850 | 0.001 | - | |
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| 1.5912 | 5900 | 0.0069 | - | |
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| 1.6046 | 5950 | 0.0124 | - | |
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| 1.6181 | 6000 | 0.0026 | - | |
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| 1.6316 | 6050 | 0.0143 | - | |
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| 1.6451 | 6100 | 0.0005 | - | |
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| 1.6586 | 6150 | 0.0362 | - | |
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| 1.6721 | 6200 | 0.0002 | - | |
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| 1.6855 | 6250 | 0.0608 | - | |
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| 1.6990 | 6300 | 0.0006 | - | |
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| 1.7125 | 6350 | 0.0003 | - | |
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| 1.7260 | 6400 | 0.0041 | - | |
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| 1.7395 | 6450 | 0.0045 | - | |
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| 1.7530 | 6500 | 0.0005 | - | |
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| 1.7665 | 6550 | 0.0014 | - | |
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| 1.7799 | 6600 | 0.0004 | - | |
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| 1.7934 | 6650 | 0.0211 | - | |
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| 1.8069 | 6700 | 0.0002 | - | |
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| 1.8204 | 6750 | 0.0048 | - | |
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| 1.8339 | 6800 | 0.0368 | - | |
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| 1.8474 | 6850 | 0.0107 | - | |
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| 1.8608 | 6900 | 0.0045 | - | |
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| 1.8743 | 6950 | 0.0062 | - | |
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| 1.8878 | 7000 | 0.0003 | - | |
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| 1.9013 | 7050 | 0.0001 | - | |
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| 1.9148 | 7100 | 0.0096 | - | |
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| 1.9283 | 7150 | 0.0008 | - | |
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| 1.9417 | 7200 | 0.0184 | - | |
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| 1.9552 | 7250 | 0.0006 | - | |
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| 1.9687 | 7300 | 0.0291 | - | |
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| 1.9822 | 7350 | 0.0335 | - | |
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| 1.9957 | 7400 | 0.0149 | - | |
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| **2.0** | **7416** | **-** | **0.2666** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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