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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-all-labs |
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metrics: |
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- accuracy |
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widget: |
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- text: return list(dict.fromkeys(sorted(it))) |
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- text: ' perms = all_permutations_substrings(string)\n result = perms & set(words)\n return |
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set(i for i in words if i in perms)' |
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- text: return [l for i, l in enumerate(it) if i == it.index(l)] |
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- text: " unique_items = set(it)\n return sorted(list(unique_items))" |
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- text: " seen = set()\n result = []\n for word in it:\n if word not\ |
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\ in seen:\n result.append(word)\n seen.add(word)\n return\ |
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\ result" |
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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: 6.0133985248367114 |
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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: 0.019 |
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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-all-labs](https://huggingface.co/datasets/hojzas/proj4-all-labs) 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:** 7 classes |
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- **Training Dataset:** [hojzas/proj4-all-labs](https://huggingface.co/datasets/hojzas/proj4-all-labs) |
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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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| 3 | <ul><li>' it = list(dict.fromkeys(it))\n it.sort()\n return it'</li><li>' sequence = []\n for i in it:\n if i in sequence:\n pass\n else:\n sequence.append(i)\n sequence.sort()\n return sequence'</li><li>' unique = list(set(it))\n unique.sort()\n return unique'</li></ul> | |
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| 2 | <ul><li>'return sorted(list({word : it.count(word) for (word) in set(it)}.keys())) '</li><li>'return list(dict.fromkeys(sorted(it)))'</li><li>'return sorted((list(dict.fromkeys(it)))) '</li></ul> | |
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| 4 | <ul><li>' unique_items = set(it)\n return sorted(list(unique_items))'</li><li>' letters = set(it)\n sorted_letters = sorted(letters)\n return sorted_letters'</li><li>'return list(sorted(set(it)))'</li></ul> | |
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| 5 | <ul><li>' outputSequence = []\n for input in it:\n found = 0\n for output in outputSequence:\n if output == input:\n found = 1\n break\n if not found:\n outputSequence.append(input)\n return outputSequence'</li><li>' uniq = []\n for char in it:\n if not char in uniq:\n uniq.append(char)\n return uniq'</li><li>'return sorted(set(it), key=lambda y: it.index(y)) '</li></ul> | |
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| 6 | <ul><li>'return [tmp for tmp in dict.fromkeys(it).keys()]'</li><li>'return [i for i in dict.fromkeys(it)]'</li><li>'return list(dict.fromkeys(it))'</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-all-labs") |
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# Run inference |
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preds = model("return list(dict.fromkeys(sorted(it)))") |
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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 | 2 | 25.0515 | 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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| 2 | 8 | |
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| 3 | 10 | |
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| 4 | 9 | |
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| 5 | 13 | |
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| 6 | 8 | |
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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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| 0.0041 | 1 | 0.1745 | - | |
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| 0.2058 | 50 | 0.0355 | - | |
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| 0.4115 | 100 | 0.0168 | - | |
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| 0.6173 | 150 | 0.0042 | - | |
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| 0.8230 | 200 | 0.0075 | - | |
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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**: 0.006 kg of CO2 |
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- **Hours Used**: 0.019 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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