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--- |
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base_model: BAAI/bge-small-en-v1.5 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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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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widget: |
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- text: 'I have owned this NAS for almost a year now and actually purchased a second |
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one It works flawlessly and QNAP live tech support is superb There is also a fairly |
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comprehensive forum for users as well I have slowly upgraded my capacities as |
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newer larger capacity drives have come out on the market All have been recognized |
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and the space expanded without a hitch I highly recommend this product ' |
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- text: Good as expected |
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- text: 'This is a very good video editing package In the past I ve only used Corel |
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video editing products but Cyberlink s offering is on par It offers similar options |
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but they are different enough for me to want to use both products depending on |
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what I m trying to achieve There are quick uploading options that make it very |
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easy to get video onto Youtube and other online video sites ' |
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- text: Works great |
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- text: 'This is my favorite crack open the computer and amuse myself for a few hours |
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software Easy to pick up if you have no prior experience with computer animation |
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but advanced enough that someone with the right skills could pull together an |
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impressive movie ' |
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inference: true |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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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 [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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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:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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>'Have used Turbo Tax for years Never a problem I m pretty concerned now with the news that many of their users had their returns hacked by people who gained access to Turbo Tax and stole the information Not sure I will use it next year until I research how serious this is was '</li><li>'Can t beat an Apple computer Like P KB best by test '</li><li>'Works for Mac or Pc but not on widows '</li></ul> | |
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| 1 | <ul><li>'Would not install activation code not accepted Returned it '</li><li>'Worth all four of the software programs which are included in this product '</li><li>'The marketing information makes this software look like it should be fabulous lots of useful features that I would love to experiment with However the software just doesn t work I will keep using my very old JASC version of this software instead '</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("selina09/yt_setfit") |
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# Run inference |
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preds = model("Works great") |
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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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*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 |
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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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*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 | 1 | 34.9207 | 102 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 123 | |
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| 1 | 41 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (10, 10) |
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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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- 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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| 0.0019 | 1 | 0.2503 | - | |
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| 0.0942 | 50 | 0.2406 | - | |
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| 0.1883 | 100 | 0.2029 | - | |
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| 0.2825 | 150 | 0.2207 | - | |
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| 0.3766 | 200 | 0.1612 | - | |
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| 0.4708 | 250 | 0.0725 | - | |
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| 0.5650 | 300 | 0.0163 | - | |
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| 0.6591 | 350 | 0.0108 | - | |
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| 0.7533 | 400 | 0.0153 | - | |
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| 0.8475 | 450 | 0.0486 | - | |
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| 0.9416 | 500 | 0.0191 | - | |
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| 1.0358 | 550 | 0.0207 | - | |
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| 1.1299 | 600 | 0.0148 | - | |
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| 1.2241 | 650 | 0.0031 | - | |
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| 1.3183 | 700 | 0.001 | - | |
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| 1.4124 | 750 | 0.0287 | - | |
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| 1.5066 | 800 | 0.0146 | - | |
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| 1.6008 | 850 | 0.0147 | - | |
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| 1.6949 | 900 | 0.0165 | - | |
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| 1.7891 | 950 | 0.0008 | - | |
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| 1.8832 | 1000 | 0.0165 | - | |
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| 1.9774 | 1050 | 0.0007 | - | |
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| 2.0716 | 1100 | 0.0129 | - | |
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| 2.1657 | 1150 | 0.0143 | - | |
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| 2.2599 | 1200 | 0.0006 | - | |
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| 2.3540 | 1250 | 0.0008 | - | |
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| 2.4482 | 1300 | 0.0047 | - | |
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| 2.5424 | 1350 | 0.0005 | - | |
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| 2.6365 | 1400 | 0.0116 | - | |
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| 2.7307 | 1450 | 0.0093 | - | |
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| 2.8249 | 1500 | 0.0211 | - | |
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| 2.9190 | 1550 | 0.0076 | - | |
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| 3.0132 | 1600 | 0.0047 | - | |
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| 3.1073 | 1650 | 0.0005 | - | |
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| 3.2015 | 1700 | 0.0064 | - | |
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| 3.2957 | 1750 | 0.014 | - | |
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| 3.3898 | 1800 | 0.0479 | - | |
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| 3.4840 | 1850 | 0.0005 | - | |
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| 3.5782 | 1900 | 0.0045 | - | |
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| 3.6723 | 1950 | 0.0188 | - | |
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| 3.7665 | 2000 | 0.0004 | - | |
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| 3.8606 | 2050 | 0.0122 | - | |
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| 3.9548 | 2100 | 0.0004 | - | |
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| 4.0490 | 2150 | 0.008 | - | |
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| 4.1431 | 2200 | 0.0245 | - | |
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| 4.2373 | 2250 | 0.005 | - | |
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| 4.3315 | 2300 | 0.0244 | - | |
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| 4.4256 | 2350 | 0.0208 | - | |
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| 4.5198 | 2400 | 0.0237 | - | |
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| 4.6139 | 2450 | 0.0005 | - | |
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| 4.7081 | 2500 | 0.0004 | - | |
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| 4.8023 | 2550 | 0.02 | - | |
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| 4.8964 | 2600 | 0.0004 | - | |
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| 4.9906 | 2650 | 0.0067 | - | |
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| 5.0847 | 2700 | 0.0099 | - | |
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| 5.1789 | 2750 | 0.0138 | - | |
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| 5.2731 | 2800 | 0.0192 | - | |
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| 5.3672 | 2850 | 0.0217 | - | |
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| 5.4614 | 2900 | 0.0056 | - | |
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| 5.5556 | 2950 | 0.0003 | - | |
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| 5.6497 | 3000 | 0.0052 | - | |
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| 5.7439 | 3050 | 0.0123 | - | |
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| 5.8380 | 3100 | 0.0136 | - | |
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| 5.9322 | 3150 | 0.0221 | - | |
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| 6.0264 | 3200 | 0.0235 | - | |
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| 6.1205 | 3250 | 0.0144 | - | |
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| 6.2147 | 3300 | 0.0174 | - | |
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| 6.3089 | 3350 | 0.007 | - | |
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| 6.4030 | 3400 | 0.0044 | - | |
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| 6.4972 | 3450 | 0.0003 | - | |
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| 6.5913 | 3500 | 0.007 | - | |
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| 6.6855 | 3550 | 0.0004 | - | |
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| 6.7797 | 3600 | 0.0384 | - | |
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| 6.8738 | 3650 | 0.0055 | - | |
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| 6.9680 | 3700 | 0.0056 | - | |
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| 7.0621 | 3750 | 0.0118 | - | |
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| 7.1563 | 3800 | 0.0143 | - | |
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| 7.2505 | 3850 | 0.0289 | - | |
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| 7.3446 | 3900 | 0.0301 | - | |
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| 7.4388 | 3950 | 0.0119 | - | |
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| 7.5330 | 4000 | 0.012 | - | |
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| 7.6271 | 4050 | 0.0138 | - | |
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| 7.7213 | 4100 | 0.0148 | - | |
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| 7.8154 | 4150 | 0.0003 | - | |
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| 7.9096 | 4200 | 0.0268 | - | |
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| 8.0038 | 4250 | 0.0131 | - | |
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| 8.0979 | 4300 | 0.0237 | - | |
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| 8.1921 | 4350 | 0.0004 | - | |
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| 8.2863 | 4400 | 0.0211 | - | |
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| 8.3804 | 4450 | 0.0092 | - | |
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| 8.4746 | 4500 | 0.005 | - | |
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| 8.5687 | 4550 | 0.0056 | - | |
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| 8.6629 | 4600 | 0.0168 | - | |
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| 8.7571 | 4650 | 0.0045 | - | |
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| 8.8512 | 4700 | 0.0184 | - | |
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| 8.9454 | 4750 | 0.0049 | - | |
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| 9.0395 | 4800 | 0.0047 | - | |
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| 9.1337 | 4850 | 0.0099 | - | |
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| 9.2279 | 4900 | 0.0054 | - | |
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| 9.3220 | 4950 | 0.0185 | - | |
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| 9.4162 | 5000 | 0.005 | - | |
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| 9.5104 | 5050 | 0.0004 | - | |
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| 9.6045 | 5100 | 0.013 | - | |
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| 9.6987 | 5150 | 0.0002 | - | |
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| 9.7928 | 5200 | 0.0187 | - | |
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| 9.8870 | 5250 | 0.0003 | - | |
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| 9.9812 | 5300 | 0.0081 | - | |
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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: 3.0.1 |
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- Transformers: 4.40.2 |
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- PyTorch: 2.4.0+cu121 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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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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