--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer datasets: - hojzas/proj8-multilabel metrics: - accuracy widget: - text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys_used = {}\n for item in iterable:\n rp = repr(key(item))\n if rp not in keys_used.keys():\n keys_used[rp] = repr(item)\n yield item' - text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))' - text: 'def first_with_given_key(iterable, key=repr):\n set_of_keys = set()\n lambda_key = (lambda x: key(x))\n for item in iterable:\n key = lambda_key(item)\n try:\n key_for_set = hash(key)\n except TypeError:\n key_for_set = repr(key)\n if key_for_set in set_of_keys:\n continue\n set_of_keys.add(key_for_set)\n yield item' - text: 'def first_with_given_key(iterable, key = lambda x: x):\n found_keys={}\n for i in iterable:\n if key(i) not in found_keys.keys():\n found_keys[key(i)]=i\n yield i' - text: 'def first_with_given_key(the_iterable, key=lambda x: x):\n temp_keys=[]\n for i in range(len(the_iterable)):\n if (key(the_iterable[i]) not in temp_keys):\n temp_keys.append(key(the_iterable[i]))\n yield the_iterable[i]\n del temp_keys' pipeline_tag: text-classification inference: false co2_eq_emissions: emissions: 0.2716104726718793 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz ram_total_size: 251.49160385131836 hours_used: 0.005 base_model: sentence-transformers/paraphrase-mpnet-base-v2 --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel) dataset 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. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a OneVsRestClassifier instance - **Maximum Sequence Length:** 512 tokens - **Training Dataset:** [hojzas/proj8-multilabel](https://huggingface.co/datasets/hojzas/proj8-multilabel) ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("hojzas/setfit-proj8-multilabel") # Run inference preds = model("def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 43 | 92.5185 | 125 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0147 | 1 | 0.3001 | - | | 0.7353 | 50 | 0.0104 | - | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Carbon Emitted**: 0.000 kg of CO2 - **Hours Used**: 0.005 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: No GPU used - **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz - **RAM Size**: 251.49 GB ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.2.2 - Transformers: 4.36.1 - PyTorch: 2.1.2+cu121 - Datasets: 2.14.7 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```