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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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base_model: sentence-transformers/all-mpnet-base-v2 |
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datasets: |
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- hojzas/proj9-lab1 |
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metrics: |
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- accuracy |
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widget: |
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- text: ' try:\n async with aiohttp.ClientSession(headers = fake_headers) |
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as session:\n async with session.get(url) as response:\n outcome |
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= response.status\n except Exception as e:\n outcome = e.__class__.__name__\n return |
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(outcome, url)' |
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- text: ' async with aiohttp.ClientSession() as current_session:\n pairs |
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= [fetch_url(current_session, url) for url in url_list]\n res_pairs |
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= await asyncio.gather(*pairs)\n return res_pairs' |
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- text: tasks = [asyncio.create_task(fetch_single_url(url)) for url in urls]\n results |
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= asyncio.gather(*tasks)\n return results |
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- text: ' coros = [get_url(url) for url in urls]\n results = asyncio.get_event_loop().run_until_complete(asyncio.gather(*coros))\n return |
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results' |
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- text: ' tasks = [download_url(url) for url in urls]\n results = asyncio.gather(*tasks)\n return |
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results' |
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pipeline_tag: text-classification |
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inference: true |
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--- |
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# SetFit with sentence-transformers/all-mpnet-base-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj9-lab1](https://huggingface.co/datasets/hojzas/proj9-lab1) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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:** 384 tokens |
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- **Number of Classes:** 2 classes |
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- **Training Dataset:** [hojzas/proj9-lab1](https://huggingface.co/datasets/hojzas/proj9-lab1) |
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<!-- - **Language:** 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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| 0 | <ul><li>' async with aiohttp.ClientSession() as session:\\n tasks = [fetch_url(session, url) for url in urls]\\n return await asyncio.gather(*tasks)'</li><li>' tasks = [download_url(url) for url in urls]\\n results = await asyncio.gather(*tasks)\\n return results'</li><li>' async with ClientSession() as client_session:\\n tasks = [asyncio.create_task(fetch_single_url(client_session, url)) for url in urls]\\n results = await asyncio.gather(*tasks)\\n return results'</li></ul> | |
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| 1 | <ul><li>' coros = [get_url(url) for url in urls]\\n results = asyncio.get_event_loop().run_until_complete(asyncio.gather(*coros))\\n return results'</li><li>' with aiohttp.ClientSession() as client:\\n tasks = [retrieve_data(client, target) for target in urls]\\n outcomes = asyncio.gather(*tasks)\\n return outcomes'</li><li>'tasks = [asyncio.create_task(fetch_single_url(url)) for url in urls]\\n results = asyncio.gather(*tasks)\\n return results'</li></ul> | |
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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("hojzas/proj9") |
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# Run inference |
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preds = model(" tasks = [download_url(url) for url in urls]\n results = asyncio.gather(*tasks)\n return results") |
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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 | 18 | 37.7333 | 76 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 8 | |
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| 1 | 7 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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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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- seed: 42 |
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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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| 0.0263 | 1 | 0.3316 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.40.2 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.19.1 |
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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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