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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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datasets: |
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- hojzas/proj4-match_permutations_substrings-lab1 |
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metrics: |
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- accuracy |
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widget: |
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- text: ' counter = defaultdict(int)\n for character in string:\n counter[character] |
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+= 1\n result = set()\n for word in words:\n word_counter = defaultdict(int)\n for |
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character in word:\n word_counter[character] += 1\n for key, |
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count in word_counter.items():\n if counter[key] < count:\n break\n else:\n result.add(word)\n return |
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result' |
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- text: ' perms = all_permutations_substrings(string)\n return set([x for x |
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in list(perms) + words if x in list(perms) and x in words])' |
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- text: ' perms = all_permutations_substrings(string)\n return set( perms.intersection(words))' |
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- text: ' perms = all_permutations_substrings(string)\n for x in perms:\n words.append(x)\n dupes |
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= [x for n, x in enumerate(words) if x in words[:n]] \n return set(dupes)' |
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- text: ' perms = all_permutations_substrings(string)\n to_return = []\n for |
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w in words:\n if w in perms:\n to_return.append(w)\n to_return |
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= set(to_return)\n return to_return' |
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pipeline_tag: text-classification |
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inference: true |
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co2_eq_emissions: |
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emissions: 1.8025910115185662 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz |
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ram_total_size: 251.49161911010742 |
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hours_used: 0.006 |
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hardware_used: 4 x NVIDIA RTX A5000 |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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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/proj4-match_permutations_substrings-lab1](https://huggingface.co/datasets/hojzas/proj4-match_permutations_substrings-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/proj4-match_permutations_substrings-lab1](https://huggingface.co/datasets/hojzas/proj4-match_permutations_substrings-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>" perms = all_permutations_substrings(string)\\n return set(''.join(perm) for word in words for perm in perms if word == perm)"</li><li>' perms = all_permutations_substrings(string)\\n out = set()\\n for w in words:\\n for s in perms:\\n if w == s:\\n out.add(w)\\n return out'</li><li>' perms = all_permutations_substrings(string)\\n return set(word for word in words if word in perms)'</li></ul> | |
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| 1 | <ul><li>' perms = all_permutations_substrings(string)\\n return perms.intersection(words)'</li><li>' perms = all_permutations_substrings(string)\\n return set.intersection(perms,words)'</li><li>' perms = all_permutations_substrings(string)\\n return set(perms).intersection(words)'</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/proj4-match_permutations_substrings-lab1") |
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# Run inference |
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preds = model(" perms = all_permutations_substrings(string)\n return set( perms.intersection(words))") |
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``` |
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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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## 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 | 12 | 29.1633 | 140 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 35 | |
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| 1 | 14 | |
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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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| 0.0081 | 1 | 0.3668 | - | |
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| 0.4065 | 50 | 0.0048 | - | |
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| 0.8130 | 100 | 0.0014 | - | |
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Carbon Emitted**: 0.002 kg of CO2 |
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- **Hours Used**: 0.006 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 4 x NVIDIA RTX A5000 |
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- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz |
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- **RAM Size**: 251.49 GB |
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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: 2.2.2 |
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- Transformers: 4.36.1 |
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- PyTorch: 2.1.2+cu121 |
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- Datasets: 2.14.7 |
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- Tokenizers: 0.15.1 |
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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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