| | --- |
| | library_name: setfit |
| | tags: |
| | - setfit |
| | - sentence-transformers |
| | - text-classification |
| | - generated_from_setfit_trainer |
| | datasets: |
| | - hojzas/proj4-match_permutations_substrings-lab1 |
| | metrics: |
| | - accuracy |
| | widget: |
| | - text: ' counter = defaultdict(int)\n for character in string:\n counter[character] |
| | += 1\n result = set()\n for word in words:\n word_counter = defaultdict(int)\n for |
| | character in word:\n word_counter[character] += 1\n for key, |
| | count in word_counter.items():\n if counter[key] < count:\n break\n else:\n result.add(word)\n return |
| | result' |
| | - text: ' perms = all_permutations_substrings(string)\n return set([x for x |
| | in list(perms) + words if x in list(perms) and x in words])' |
| | - text: ' perms = all_permutations_substrings(string)\n return set( perms.intersection(words))' |
| | - text: ' perms = all_permutations_substrings(string)\n for x in perms:\n words.append(x)\n dupes |
| | = [x for n, x in enumerate(words) if x in words[:n]] \n return set(dupes)' |
| | - text: ' perms = all_permutations_substrings(string)\n to_return = []\n for |
| | w in words:\n if w in perms:\n to_return.append(w)\n to_return |
| | = set(to_return)\n return to_return' |
| | pipeline_tag: text-classification |
| | inference: true |
| | co2_eq_emissions: |
| | emissions: 1.8025910115185662 |
| | source: codecarbon |
| | training_type: fine-tuning |
| | on_cloud: false |
| | cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz |
| | ram_total_size: 251.49161911010742 |
| | hours_used: 0.006 |
| | hardware_used: 4 x NVIDIA RTX A5000 |
| | base_model: sentence-transformers/all-mpnet-base-v2 |
| | --- |
| | |
| | # SetFit with sentence-transformers/all-mpnet-base-v2 |
| |
|
| | 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. |
| |
|
| | 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) |
| | - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| | - **Maximum Sequence Length:** 384 tokens |
| | - **Number of Classes:** 2 classes |
| | - **Training Dataset:** [hojzas/proj4-match_permutations_substrings-lab1](https://huggingface.co/datasets/hojzas/proj4-match_permutations_substrings-lab1) |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### 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) |
| |
|
| | ### Model Labels |
| | | Label | Examples | |
| | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | 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> | |
| | | 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> | |
| |
|
| | ## 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/proj4-match_permutations_substrings-lab1") |
| | # Run inference |
| | preds = model(" perms = all_permutations_substrings(string)\n return set( perms.intersection(words))") |
| | ``` |
| |
|
| | <!-- |
| | ### Downstream Use |
| |
|
| | *List how someone could finetune this model on their own dataset.* |
| | --> |
| |
|
| | <!-- |
| | ### 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 |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Set Metrics |
| | | Training set | Min | Median | Max | |
| | |:-------------|:----|:--------|:----| |
| | | Word count | 12 | 29.1633 | 140 | |
| |
|
| | | Label | Training Sample Count | |
| | |:------|:----------------------| |
| | | 0 | 35 | |
| | | 1 | 14 | |
| |
|
| | ### 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 | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0081 | 1 | 0.3668 | - | |
| | | 0.4065 | 50 | 0.0048 | - | |
| | | 0.8130 | 100 | 0.0014 | - | |
| | |
| | ### Environmental Impact |
| | Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
| | - **Carbon Emitted**: 0.002 kg of CO2 |
| | - **Hours Used**: 0.006 hours |
| | |
| | ### Training Hardware |
| | - **On Cloud**: No |
| | - **GPU Model**: 4 x NVIDIA RTX A5000 |
| | - **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} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
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| | *Clearly define terms in order to be accessible across audiences.* |
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| | ## Model Card Authors |
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| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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