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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/proj8-lab2 |
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metrics: |
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- accuracy |
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widget: |
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- text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys_used = {}\n for |
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item in iterable:\n rp = repr(key(item))\n if rp not in keys_used.keys():\n keys_used[rp] |
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= repr(item)\n yield item' |
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- text: 'def first_with_given_key(iterable, key=lambda x: x):\n keys=[]\n for |
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i in iterable:\n if key(i) not in keys:\n yield i\n keys.append(key(i))' |
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- text: 'def first_with_given_key(lst, key = lambda x: x):\n res = set()\n for |
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i in lst:\n if repr(key(i)) not in res:\n res.add(repr(key(i)))\n yield |
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i' |
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- text: def first_with_given_key(iterable, key=repr):\n used_keys = dict()\n get_key |
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= return_key(key)\n for index in iterable:\n index_key = get_key(index)\n if |
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index_key in used_keys.keys():\n continue\n try:\n used_keys[hash(index_key)] |
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= repr(index)\n except TypeError:\n used_keys[repr(index_key)] |
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= repr(index)\n yield index |
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- text: 'def first_with_given_key(the_iterable, key=lambda x: x):\n temp_keys=[]\n for |
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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 |
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the_iterable[i]\n del temp_keys' |
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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: 2.099245090500422 |
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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/proj8-lab2](https://huggingface.co/datasets/hojzas/proj8-lab2) 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:** 3 classes |
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- **Training Dataset:** [hojzas/proj8-lab2](https://huggingface.co/datasets/hojzas/proj8-lab2) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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>'def first_with_given_key(iterable, key=lambda x: x):\\n keys_in_list = []\\n for it in iterable:\\n if key(it) not in keys_in_list:\\n keys_in_list.append(key(it))\\n yield it'</li><li>'def first_with_given_key(iterable, key=lambda value: value):\\n it = iter(iterable)\\n saved_keys = []\\n while True:\\n try:\\n value = next(it)\\n if key(value) not in saved_keys:\\n saved_keys.append(key(value))\\n yield value\\n except StopIteration:\\n break'</li><li>'def first_with_given_key(iterable, key=None):\\n if key is None:\\n key = lambda x: x\\n item_list = []\\n key_set = set()\\n for item in iterable:\\n generated_item = key(item)\\n if generated_item not in item_list:\\n item_list.append(generated_item)\\n yield item'</li></ul> | |
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| 2 | <ul><li>'def first_with_given_key(iterable, key=repr):\\n prev_keys = {}\\n lamb_key = lambda item: key(item)\\n for obj in iterable:\\n obj_key = lamb_key(obj)\\n if(obj_key) in prev_keys.keys():\\n continue\\n try:\\n prev_keys[hash(obj_key)] = repr(obj)\\n except TypeError:\\n prev_keys[repr(obj_key)] = repr(obj)\\n yield obj'</li><li>'def first_with_given_key(iterable, key=repr):\\n used_keys = dict()\\n get_key = lambda index: key(index)\\n for index in iterable:\\n index_key = get_key(index)\\n if index_key in used_keys.keys():\\n continue\\n try:\\n used_keys[hash(index_key)] = repr(index)\\n except TypeError:\\n used_keys[repr(index_key)] = repr(index)\\n yield index'</li><li>'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'</li></ul> | |
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| 1 | <ul><li>'def first_with_given_key(lst, key = lambda x: x):\\n res = set()\\n for i in lst:\\n if repr(key(i)) not in res:\\n res.add(repr(key(i)))\\n yield i'</li><li>'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'</li><li>'def first_with_given_key(iterable, key=None):\\n if key is None:\\n key = identity\\n appeared_keys = set()\\n for item in iterable:\\n generated_key = key(item)\\n if not generated_key.__hash__:\\n generated_key = repr(generated_key)\\n if generated_key not in appeared_keys:\\n appeared_keys.add(generated_key)\\n yield item'</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/proj8-lab2") |
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# Run inference |
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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))") |
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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 | 43 | 92.2069 | 125 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 13 | |
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| 1 | 8 | |
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| 2 | 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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| 0.0137 | 1 | 0.4142 | - | |
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| 0.6849 | 50 | 0.0024 | - | |
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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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