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--- |
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language: |
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- en |
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license: apache-2.0 |
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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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- sst2 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: 'this is a story of two misfits who do n''t stand a chance alone , but together |
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they are magnificent . ' |
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- text: 'it does n''t believe in itself , it has no sense of humor ... it ''s just |
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plain bored . ' |
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- text: 'the band ''s courage in the face of official repression is inspiring , especially |
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for aging hippies ( this one included ) . ' |
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- text: 'a fast , funny , highly enjoyable movie . ' |
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- text: 'the movie achieves as great an impact by keeping these thoughts hidden as |
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... ( quills ) did by showing them . ' |
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pipeline_tag: text-classification |
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co2_eq_emissions: |
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emissions: 2.768308759172054 |
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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: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 0.072 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: sst2 |
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type: sst2 |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.7512953367875648 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [sst2](https://huggingface.co/datasets/sst2) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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:** 256 tokens |
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- **Number of Classes:** 2 classes |
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- **Training Dataset:** [sst2](https://huggingface.co/datasets/sst2) |
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- **Language:** en |
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- **License:** apache-2.0 |
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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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| negative | <ul><li>'a tough pill to swallow and '</li><li>'indignation '</li><li>'that the typical hollywood disregard for historical truth and realism is at work here '</li></ul> | |
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| positive | <ul><li>"a moving experience for people who have n't read the book "</li><li>'in the best possible senses of both those words '</li><li>'to serve the work especially well '</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.7513 | |
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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 🤗 Hub |
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model = SetFitModel.from_pretrained("tomaarsen/setfit-all-MiniLM-L6-v2-sst2-8-shot") |
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# Run inference |
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preds = model("a fast , funny , highly enjoyable movie . ") |
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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 | 2 | 10.2812 | 36 | |
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| Label | Training Sample Count | |
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|:---------|:----------------------| |
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| negative | 32 | |
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| positive | 32 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (3, 3) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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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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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:----------:|:------:|:-------------:|:---------------:| |
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| 0.0076 | 1 | 0.3787 | - | |
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| 0.0758 | 10 | 0.2855 | - | |
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| 0.1515 | 20 | 0.3458 | 0.29 | |
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| 0.2273 | 30 | 0.2496 | - | |
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| 0.3030 | 40 | 0.2398 | 0.2482 | |
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| 0.3788 | 50 | 0.2068 | - | |
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| 0.4545 | 60 | 0.2471 | 0.244 | |
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| 0.5303 | 70 | 0.2053 | - | |
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| **0.6061** | **80** | **0.1802** | **0.2361** | |
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| 0.6818 | 90 | 0.0767 | - | |
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| 0.7576 | 100 | 0.0279 | 0.2365 | |
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| 0.8333 | 110 | 0.0192 | - | |
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| 0.9091 | 120 | 0.0095 | 0.2527 | |
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| 0.9848 | 130 | 0.0076 | - | |
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| 1.0606 | 140 | 0.0082 | 0.2651 | |
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| 1.1364 | 150 | 0.0068 | - | |
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| 1.2121 | 160 | 0.0052 | 0.2722 | |
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| 1.2879 | 170 | 0.0029 | - | |
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| 1.3636 | 180 | 0.0042 | 0.273 | |
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| 1.4394 | 190 | 0.0026 | - | |
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| 1.5152 | 200 | 0.0036 | 0.2761 | |
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| 1.5909 | 210 | 0.0044 | - | |
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| 1.6667 | 220 | 0.0027 | 0.2796 | |
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| 1.7424 | 230 | 0.0025 | - | |
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| 1.8182 | 240 | 0.0025 | 0.2817 | |
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| 1.8939 | 250 | 0.003 | - | |
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| 1.9697 | 260 | 0.0026 | 0.2817 | |
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| 2.0455 | 270 | 0.0035 | - | |
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| 2.1212 | 280 | 0.002 | 0.2816 | |
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| 2.1970 | 290 | 0.0023 | - | |
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| 2.2727 | 300 | 0.0016 | 0.2821 | |
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| 2.3485 | 310 | 0.0023 | - | |
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| 2.4242 | 320 | 0.0015 | 0.2838 | |
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| 2.5 | 330 | 0.0014 | - | |
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| 2.5758 | 340 | 0.002 | 0.2842 | |
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| 2.6515 | 350 | 0.002 | - | |
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| 2.7273 | 360 | 0.0013 | 0.2847 | |
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| 2.8030 | 370 | 0.0009 | - | |
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| 2.8788 | 380 | 0.0018 | 0.2857 | |
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| 2.9545 | 390 | 0.0016 | - | |
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* The bold row denotes the saved checkpoint. |
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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**: 0.003 kg of CO2 |
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- **Hours Used**: 0.072 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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### Framework Versions |
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- Python: 3.9.16 |
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- SetFit: 1.0.0.dev0 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.29.0 |
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- PyTorch: 1.13.1+cu117 |
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- Datasets: 2.15.0 |
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- Tokenizers: 0.13.3 |
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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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