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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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metrics: |
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- accuracy |
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widget: |
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- text: Upgrade all installed packages with superuser privileges |
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- text: Install package 'vim' as superuser |
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- text: Remove package 'firefox' with superuser privileges |
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- text: Change permissions of directory 'docs' to writable |
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- text: Update package lists using superuser privileges |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.0 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** 512 tokens |
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- **Number of Classes:** 30 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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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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| ls | <ul><li>'List all files and directories'</li><li>'Show files in the current directory'</li><li>'Display contents of the current directory'</li></ul> | |
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| cd | <ul><li>'Change to the specified directory'</li><li>'Move to the home directory'</li><li>'Navigate to the specified directory path'</li></ul> | |
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| mkdir docs | <ul><li>"Create a new directory named 'docs'"</li></ul> | |
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| mkdir projects | <ul><li>"Make a directory named 'projects'"</li></ul> | |
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| mkdir data | <ul><li>"Create a folder called 'data'"</li></ul> | |
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| mkdir images | <ul><li>"Make a directory named 'images'"</li></ul> | |
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| mkdir scripts | <ul><li>"Create a new folder named 'scripts'"</li></ul> | |
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| rm example.txt | <ul><li>"Remove the file named 'example.txt'"</li></ul> | |
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| rm temp.txt | <ul><li>"Delete the file called 'temp.txt'"</li></ul> | |
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| rm file1 | <ul><li>"Remove the file named 'file1'"</li></ul> | |
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| rm file2 | <ul><li>"Delete the file named 'file2'"</li></ul> | |
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| rm backup.txt | <ul><li>"Remove the file named 'backup.txt'"</li></ul> | |
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| cp file1 /destination | <ul><li>'Copy file1 to directory /destination'</li></ul> | |
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| cp file2 /backup | <ul><li>'Duplicate file2 to directory /backup'</li></ul> | |
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| cp file3 /archive | <ul><li>'Copy file3 to folder /archive'</li></ul> | |
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| cp file4 /temp | <ul><li>'Duplicate file4 to folder /temp'</li></ul> | |
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| cp file5 /images | <ul><li>'Copy file5 to directory /images'</li></ul> | |
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| mv file2 /new_location | <ul><li>'Move file2 to directory /new_location'</li></ul> | |
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| mv file3 /backup | <ul><li>'Transfer file3 to directory /backup'</li></ul> | |
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| mv file4 /archive | <ul><li>'Move file4 to folder /archive'</li></ul> | |
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| mv file5 /temp | <ul><li>'Transfer file5 to folder /temp'</li></ul> | |
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| mv file6 /images | <ul><li>'Move file6 to directory /images'</li></ul> | |
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| cat README.md | <ul><li>"Display the contents of file 'README.md'"</li></ul> | |
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| cat notes.txt | <ul><li>"Show the content of file 'notes.txt'"</li></ul> | |
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| cat data.csv | <ul><li>"Print the contents of file 'data.csv'"</li></ul> | |
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| cat script.sh | <ul><li>"Display the content of file 'script.sh'"</li></ul> | |
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| cat config.ini | <ul><li>"Show the contents of file 'config.ini'"</li></ul> | |
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| grep 'pattern' data.txt | <ul><li>"Search for 'pattern' in file 'data.txt'"</li></ul> | |
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| grep 'word' text.txt | <ul><li>"Find occurrences of 'word' in file 'text.txt'"</li></ul> | |
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| grep 'keyword' document.txt | <ul><li>"Search for 'keyword' in file 'document.txt'"</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** | 0.0 | |
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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("souvenger/NLP2Linux") |
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# Run inference |
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preds = model("Install package 'vim' as superuser") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### 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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<!-- |
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## Bias, Risks and Limitations |
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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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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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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 | 5 | 5.6667 | 9 | |
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| Label | Training Sample Count | |
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|:----------------------------|:----------------------| |
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| cat README.md | 1 | |
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| cat config.ini | 1 | |
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| cat data.csv | 1 | |
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| cat notes.txt | 1 | |
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| cat script.sh | 1 | |
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| cd | 10 | |
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| cp file1 /destination | 1 | |
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| cp file2 /backup | 1 | |
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| cp file3 /archive | 1 | |
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| cp file4 /temp | 1 | |
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| cp file5 /images | 1 | |
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| grep 'keyword' document.txt | 1 | |
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| grep 'pattern' data.txt | 1 | |
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| grep 'word' text.txt | 1 | |
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| ls | 10 | |
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| mkdir data | 1 | |
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| mkdir docs | 1 | |
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| mkdir images | 1 | |
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| mkdir projects | 1 | |
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| mkdir scripts | 1 | |
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| mv file2 /new_location | 1 | |
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| mv file3 /backup | 1 | |
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| mv file4 /archive | 1 | |
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| mv file5 /temp | 1 | |
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| mv file6 /images | 1 | |
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| rm backup.txt | 1 | |
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| rm example.txt | 1 | |
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| rm file1 | 1 | |
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| rm file2 | 1 | |
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| rm temp.txt | 1 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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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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| 0.0042 | 1 | 0.1215 | - | |
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| 0.2083 | 50 | 0.0232 | - | |
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| 0.4167 | 100 | 0.01 | - | |
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| 0.625 | 150 | 0.0044 | - | |
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| 0.8333 | 200 | 0.0025 | - | |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.3.1 |
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- Transformers: 4.37.0 |
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- PyTorch: 2.1.2 |
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- Datasets: 2.1.0 |
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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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