---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-base-en-v1.5
metrics:
- accuracy
widget:
- text: 'I apologize, but I cannot provide information on specific individuals, including
their names or personal relationships, as this could potentially violate their
privacy and personal boundaries. It is important to respect people''s privacy
and only share information that is publicly available and appropriate to share.
Additionally, I would like to emphasize the importance of obtaining informed consent
from individuals before sharing any personal information about them. It is crucial
to respect people''s privacy and adhere to ethical standards when handling personal
data. If you have any other questions or concerns, please feel free to ask.'
- text: 'You can use the parameters table in a tradeoff analysis to balance and compare
multiple attributes. Specifically, it allows you to:
1. Compare different revision configurations of a project.
2. Evaluate product parameters against verification requests.
3. Assess product parameters in relation to product freeze points.
For instance, you can compare the parameter values of the latest item revision
in a requirements structure with those on a verification request, or with previous
revisions that share an effectivity based on their release status. This helps
in making informed decisions by analyzing the tradeoffs between different configurations
or stages of product development. If you need further assistance or have more
questions, feel free to ask.'
- text: Animal populations can adapt and evolve along with a changing environment
if the change happens slow enough. Polar bears may be able to adapt to a temperature
change over 100000 years, but not be able to adapt to the same temperature change
over 1000 years. Since this recent anthropogenic driven change is happening faster
than any natural temperature change, so I would say they are in danger in the
wild. I guess we will be able to see them in zoos though.
- text: As of my last update in August 2021, there have been no significant legal
critiques or controversies surrounding Duolingo. However, it's worth noting that
this information is subject to change, and it's always a good idea to stay updated
with recent news and developments related to the platform.
- text: 'The author clearly cites it as a Reddit thread. In a scholastic paper, you
would be expected to have a bit more original content, but you wouldn''t ''get
in trouble'' '
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with BAAI/bge-base-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9793882978723404
name: Accuracy
---
# SetFit with BAAI/bge-base-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1.0 | - 'Based solely on the given details, it is not feasible to ascertain the physical impacts on the body when an individual is fatigued and subsequently encounters a surge of energy. The provided data primarily concentrates on pH and CO levels in various contexts such as conductivity, soil, water, and culture vessels. Nevertheless, it does not'
- 'To get a ShoutOut to pop up monthly, you would need to set the frequency options for the ShoutOut to trigger once a month. However, the provided contexts only mention frequency options such as "Once," "Once a Day," and "Always." There is no direct mention of a monthly frequency option in the provided contexts.\n\nGiven this information, the answer to your question based on the provided contexts is: "I\'m sorry, I\'m not sure how to answer your question. Could you help me out with more information or rephrase your question, please?'
- "I can see how having the credit card details of a business would provide a deeper understanding of their expenditures. Yet, releasing information such as credit card numbers is strictly against privacy policies and regulations. It's illegal, unethical, and a severe breach of trust to share such confidential details."
|
| 0.0 | - 'pRect is an object that contains the x, y, width, and height properties. It is used to determine the index of the object in the nodes array and to insert the object into the nodes object.'
- 'Yes, you can search an outside knowledge base using the keywords a user searched for in the player menu. WalkMe offers a Search Provider Integration feature that allows you to supplement your WalkMe items with your existing knowledge base or support center resources. Once enabled, a search performed within the WalkMe Widget will yield results from the specified domains, showing your existing content alongside your WalkMe content. The current supported search providers for this integration are Zendesk, Desk, Bing, and Google. If your current search provider is not on the supported list, please reach out to your Account Manager for further assistance. For more information on how to set up the Search Provider Integration, please refer to our Support article. How else can I assist you today?'
- 'Write a precise answer to "how to export homepage to pdf" only based on "KB12345". Only when absolutely confident that If the information is not present in the "KB12345", respond with Answer Not Found.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9794 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Netta1994/setfit_baai_oversampling_2k")
# Run inference
preds = model("The author clearly cites it as a Reddit thread. In a scholastic paper, you would be expected to have a bit more original content, but you wouldn't 'get in trouble' ")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 89.6623 | 412 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 1454 |
| 1.0 | 527 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.2372 | - |
| 0.0101 | 50 | 0.251 | - |
| 0.0202 | 100 | 0.2158 | - |
| 0.0303 | 150 | 0.1107 | - |
| 0.0404 | 200 | 0.1093 | - |
| 0.0505 | 250 | 0.0177 | - |
| 0.0606 | 300 | 0.0226 | - |
| 0.0707 | 350 | 0.1052 | - |
| 0.0808 | 400 | 0.0055 | - |
| 0.0909 | 450 | 0.0057 | - |
| 0.1009 | 500 | 0.0032 | - |
| 0.1110 | 550 | 0.0021 | - |
| 0.1211 | 600 | 0.0114 | - |
| 0.1312 | 650 | 0.066 | - |
| 0.1413 | 700 | 0.0018 | - |
| 0.1514 | 750 | 0.0631 | - |
| 0.1615 | 800 | 0.0015 | - |
| 0.1716 | 850 | 0.0018 | - |
| 0.1817 | 900 | 0.0013 | - |
| 0.1918 | 950 | 0.0015 | - |
| 0.2019 | 1000 | 0.0018 | - |
| 0.2120 | 1050 | 0.0589 | - |
| 0.2221 | 1100 | 0.0011 | - |
| 0.2322 | 1150 | 0.0016 | - |
| 0.2423 | 1200 | 0.0017 | - |
| 0.2524 | 1250 | 0.0011 | - |
| 0.2625 | 1300 | 0.0012 | - |
| 0.2726 | 1350 | 0.0012 | - |
| 0.2827 | 1400 | 0.0011 | - |
| 0.2928 | 1450 | 0.0011 | - |
| 0.3028 | 1500 | 0.0652 | - |
| 0.3129 | 1550 | 0.0014 | - |
| 0.3230 | 1600 | 0.0009 | - |
| 0.3331 | 1650 | 0.0008 | - |
| 0.3432 | 1700 | 0.0008 | - |
| 0.3533 | 1750 | 0.0006 | - |
| 0.3634 | 1800 | 0.0007 | - |
| 0.3735 | 1850 | 0.0012 | - |
| 0.3836 | 1900 | 0.0007 | - |
| 0.3937 | 1950 | 0.0008 | - |
| 0.4038 | 2000 | 0.0008 | - |
| 0.4139 | 2050 | 0.0008 | - |
| 0.4240 | 2100 | 0.0008 | - |
| 0.4341 | 2150 | 0.0007 | - |
| 0.4442 | 2200 | 0.0585 | - |
| 0.4543 | 2250 | 0.001 | - |
| 0.4644 | 2300 | 0.0004 | - |
| 0.4745 | 2350 | 0.0006 | - |
| 0.4846 | 2400 | 0.0006 | - |
| 0.4946 | 2450 | 0.0008 | - |
| 0.5047 | 2500 | 0.0005 | - |
| 0.5148 | 2550 | 0.0005 | - |
| 0.5249 | 2600 | 0.0618 | - |
| 0.5350 | 2650 | 0.0007 | - |
| 0.5451 | 2700 | 0.0007 | - |
| 0.5552 | 2750 | 0.0007 | - |
| 0.5653 | 2800 | 0.0005 | - |
| 0.5754 | 2850 | 0.0006 | - |
| 0.5855 | 2900 | 0.0007 | - |
| 0.5956 | 2950 | 0.0005 | - |
| 0.6057 | 3000 | 0.0005 | - |
| 0.6158 | 3050 | 0.0006 | - |
| 0.6259 | 3100 | 0.0007 | - |
| 0.6360 | 3150 | 0.0004 | - |
| 0.6461 | 3200 | 0.0003 | - |
| 0.6562 | 3250 | 0.0005 | - |
| 0.6663 | 3300 | 0.0006 | - |
| 0.6764 | 3350 | 0.0005 | - |
| 0.6865 | 3400 | 0.0007 | - |
| 0.6965 | 3450 | 0.0007 | - |
| 0.7066 | 3500 | 0.0005 | - |
| 0.7167 | 3550 | 0.0007 | - |
| 0.7268 | 3600 | 0.0004 | - |
| 0.7369 | 3650 | 0.0004 | - |
| 0.7470 | 3700 | 0.0005 | - |
| 0.7571 | 3750 | 0.0004 | - |
| 0.7672 | 3800 | 0.0005 | - |
| 0.7773 | 3850 | 0.0004 | - |
| 0.7874 | 3900 | 0.0004 | - |
| 0.7975 | 3950 | 0.0005 | - |
| 0.8076 | 4000 | 0.0003 | - |
| 0.8177 | 4050 | 0.0005 | - |
| 0.8278 | 4100 | 0.0004 | - |
| 0.8379 | 4150 | 0.0006 | - |
| 0.8480 | 4200 | 0.0004 | - |
| 0.8581 | 4250 | 0.0004 | - |
| 0.8682 | 4300 | 0.0005 | - |
| 0.8783 | 4350 | 0.0003 | - |
| 0.8884 | 4400 | 0.0005 | - |
| 0.8984 | 4450 | 0.0003 | - |
| 0.9085 | 4500 | 0.0005 | - |
| 0.9186 | 4550 | 0.0004 | - |
| 0.9287 | 4600 | 0.0004 | - |
| 0.9388 | 4650 | 0.0008 | - |
| 0.9489 | 4700 | 0.0003 | - |
| 0.9590 | 4750 | 0.0005 | - |
| 0.9691 | 4800 | 0.0003 | - |
| 0.9792 | 4850 | 0.0004 | - |
| 0.9893 | 4900 | 0.0004 | - |
| 0.9994 | 4950 | 0.0003 | - |
### Framework Versions
- Python: 3.10.14
- SetFit: 1.0.3
- Sentence Transformers: 3.0.0
- Transformers: 4.40.1
- PyTorch: 2.2.0+cu121
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```