---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: man, product/whatever is my new best friend. i like product but the integration
of product into office and product is a lot of fun. i just spent the day feeding
it my training presentation i'm preparing in my day job and it was very helpful.
almost better than humans.
- text: that's great news! product is the perfect platform to share these advanced
product prompts and help more users get the most out of it!
- text: after only one week's trial of the new product with brand enabled, i have
replaced my default browser product that i was using for more than 7 years with
new product. i no longer need to spend a lot of time finding answers from a bunch
of search results and web pages. it's amazing
- text: very impressive. brand is finally fighting back. i am just a little worried
about the scalability of such a high context window size, since even in their
demos it took quite a while to process everything. regardless, i am very interested
in seeing what types of capabilities a >1m token size window can unleash.
- text: product the way it shows the sources is so fucking cool, this new ai is amazing
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-base-en-v1.5
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: 0.8996138996138996
name: Accuracy
- type: f1
value:
- 0.5217391304347826
- 0.5142857142857142
- 0.9478260869565217
name: F1
- type: precision
value:
- 0.42857142857142855
- 0.4090909090909091
- 0.9775784753363229
name: Precision
- type: recall
value:
- 0.6666666666666666
- 0.6923076923076923
- 0.919831223628692
name: Recall
---
# 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:** 3 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 |
|:--------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neither | - 'it might sound strange, but in my opinion, sams intelligence intimidates him from expressing himself and creating personal art. for example, since product is a masterpiece in the sense, the bar is set very high, so he might even subconsciously be unable to put anything out less'
- 'lately, i really enjoy the genre of joke that makes you say the punchline in your head.'
- 'any idea in regard to the product product not being seen? i have 1 device with it, the rest are missing it. same wufb policies.'
|
| pit | - "brand or brand are behaving like lazy interns. when you need something useful from them like researching and consolidating a large bunch of information they'll just tell you to look it up yourself or right away refuse to do the work. useless."
- 'the moment i found out what exactly product does i just uninstalled product and went back to 10'
- "at least 80% of the product stuff posted here has produced erroneous results, and many have utilized ip theft/copyright infringement in informing the model. we're not going to spend community time on it at this point."
|
| peak | - "man, product/whatever is my new best friend. i like product but the integration of product into office and product is a lot of fun. i just spent the day feeding it my training presentation i'm preparing in my day job and it was very helpful. almost better than humans."
- "excited to share my experience with product, an incredible language model by brand! from answering questions to creative writing, it's a powerful tool that amazes me every time."
- 'product in product is a game changer!! here is a list of things it can do: it can answer your questions in natural language. it can summarize content to give you a brief overview it can adjust your pcs settings it can help troubleshoot issues. 1/2'
|
## Evaluation
### Metrics
| Label | Accuracy | F1 | Precision | Recall |
|:--------|:---------|:-------------------------------------------------------------|:--------------------------------------------------------------|:------------------------------------------------------------|
| **all** | 0.8996 | [0.5217391304347826, 0.5142857142857142, 0.9478260869565217] | [0.42857142857142855, 0.4090909090909091, 0.9775784753363229] | [0.6666666666666666, 0.6923076923076923, 0.919831223628692] |
## 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("jamiehudson/725_model_v2")
# Run inference
preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 29.1484 | 90 |
| Label | Training Sample Count |
|:--------|:----------------------|
| pit | 44 |
| peak | 62 |
| neither | 150 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2383 | - |
| 0.0119 | 50 | 0.2395 | - |
| 0.0237 | 100 | 0.2129 | - |
| 0.0356 | 150 | 0.1317 | - |
| 0.0474 | 200 | 0.0695 | - |
| 0.0593 | 250 | 0.01 | - |
| 0.0711 | 300 | 0.0063 | - |
| 0.0830 | 350 | 0.0028 | - |
| 0.0948 | 400 | 0.0026 | - |
| 0.1067 | 450 | 0.0021 | - |
| 0.1185 | 500 | 0.0018 | - |
| 0.1304 | 550 | 0.0016 | - |
| 0.1422 | 600 | 0.0014 | - |
| 0.1541 | 650 | 0.0015 | - |
| 0.1659 | 700 | 0.0013 | - |
| 0.1778 | 750 | 0.0012 | - |
| 0.1896 | 800 | 0.0012 | - |
| 0.2015 | 850 | 0.0012 | - |
| 0.2133 | 900 | 0.0011 | - |
| 0.2252 | 950 | 0.0011 | - |
| 0.2370 | 1000 | 0.0009 | - |
| 0.2489 | 1050 | 0.001 | - |
| 0.2607 | 1100 | 0.0009 | - |
| 0.2726 | 1150 | 0.0008 | - |
| 0.2844 | 1200 | 0.0008 | - |
| 0.2963 | 1250 | 0.0009 | - |
| 0.3081 | 1300 | 0.0008 | - |
| 0.3200 | 1350 | 0.0007 | - |
| 0.3318 | 1400 | 0.0007 | - |
| 0.3437 | 1450 | 0.0007 | - |
| 0.3555 | 1500 | 0.0006 | - |
| 0.3674 | 1550 | 0.0007 | - |
| 0.3792 | 1600 | 0.0007 | - |
| 0.3911 | 1650 | 0.0008 | - |
| 0.4029 | 1700 | 0.0006 | - |
| 0.4148 | 1750 | 0.0006 | - |
| 0.4266 | 1800 | 0.0006 | - |
| 0.4385 | 1850 | 0.0006 | - |
| 0.4503 | 1900 | 0.0006 | - |
| 0.4622 | 1950 | 0.0006 | - |
| 0.4740 | 2000 | 0.0006 | - |
| 0.4859 | 2050 | 0.0005 | - |
| 0.4977 | 2100 | 0.0006 | - |
| 0.5096 | 2150 | 0.0006 | - |
| 0.5215 | 2200 | 0.0005 | - |
| 0.5333 | 2250 | 0.0005 | - |
| 0.5452 | 2300 | 0.0005 | - |
| 0.5570 | 2350 | 0.0006 | - |
| 0.5689 | 2400 | 0.0005 | - |
| 0.5807 | 2450 | 0.0005 | - |
| 0.5926 | 2500 | 0.0006 | - |
| 0.6044 | 2550 | 0.0006 | - |
| 0.6163 | 2600 | 0.0005 | - |
| 0.6281 | 2650 | 0.0005 | - |
| 0.6400 | 2700 | 0.0005 | - |
| 0.6518 | 2750 | 0.0005 | - |
| 0.6637 | 2800 | 0.0005 | - |
| 0.6755 | 2850 | 0.0005 | - |
| 0.6874 | 2900 | 0.0005 | - |
| 0.6992 | 2950 | 0.0004 | - |
| 0.7111 | 3000 | 0.0004 | - |
| 0.7229 | 3050 | 0.0004 | - |
| 0.7348 | 3100 | 0.0005 | - |
| 0.7466 | 3150 | 0.0005 | - |
| 0.7585 | 3200 | 0.0005 | - |
| 0.7703 | 3250 | 0.0004 | - |
| 0.7822 | 3300 | 0.0004 | - |
| 0.7940 | 3350 | 0.0004 | - |
| 0.8059 | 3400 | 0.0004 | - |
| 0.8177 | 3450 | 0.0004 | - |
| 0.8296 | 3500 | 0.0004 | - |
| 0.8414 | 3550 | 0.0004 | - |
| 0.8533 | 3600 | 0.0004 | - |
| 0.8651 | 3650 | 0.0004 | - |
| 0.8770 | 3700 | 0.0004 | - |
| 0.8888 | 3750 | 0.0004 | - |
| 0.9007 | 3800 | 0.0004 | - |
| 0.9125 | 3850 | 0.0004 | - |
| 0.9244 | 3900 | 0.0005 | - |
| 0.9362 | 3950 | 0.0004 | - |
| 0.9481 | 4000 | 0.0004 | - |
| 0.9599 | 4050 | 0.0004 | - |
| 0.9718 | 4100 | 0.0004 | - |
| 0.9836 | 4150 | 0.0004 | - |
| 0.9955 | 4200 | 0.0004 | - |
| 0.0000 | 1 | 0.2717 | - |
| 0.0013 | 50 | 0.0686 | - |
| 0.0026 | 100 | 0.088 | - |
| 0.0000 | 1 | 0.1796 | - |
| 0.0013 | 50 | 0.0584 | - |
| 0.0026 | 100 | 0.1018 | - |
| 0.0039 | 150 | 0.128 | - |
| 0.0052 | 200 | 0.0761 | - |
| 0.0065 | 250 | 0.0216 | - |
| 0.0078 | 300 | 0.1652 | - |
| 0.0091 | 350 | 0.0384 | - |
| 0.0104 | 400 | 0.0062 | - |
| 0.0117 | 450 | 0.0442 | - |
| 0.0130 | 500 | 0.0452 | - |
| 0.0143 | 550 | 0.0081 | - |
| 0.0156 | 600 | 0.0205 | - |
| 0.0169 | 650 | 0.0125 | - |
| 0.0182 | 700 | 0.0012 | - |
| 0.0195 | 750 | 0.0011 | - |
| 0.0208 | 800 | 0.0315 | - |
| 0.0221 | 850 | 0.0009 | - |
| 0.0009 | 1 | 0.0006 | - |
| 0.0429 | 50 | 0.0008 | - |
| 0.0858 | 100 | 0.0005 | - |
| 0.1288 | 150 | 0.0015 | - |
| 0.1717 | 200 | 0.0013 | - |
| 0.2146 | 250 | 0.0237 | - |
| 0.2575 | 300 | 0.0304 | - |
| 0.3004 | 350 | 0.0005 | - |
| 0.3433 | 400 | 0.0013 | - |
| 0.3863 | 450 | 0.03 | - |
| 0.4292 | 500 | 0.0005 | - |
| 0.4721 | 550 | 0.0006 | - |
| 0.5150 | 600 | 0.0005 | - |
| 0.5579 | 650 | 0.0005 | - |
| 0.6009 | 700 | 0.0004 | - |
| 0.6438 | 750 | 0.0004 | - |
| 0.6867 | 800 | 0.0004 | - |
| 0.7296 | 850 | 0.0004 | - |
| 0.7725 | 900 | 0.0004 | - |
| 0.8155 | 950 | 0.0003 | - |
| 0.8584 | 1000 | 0.0004 | - |
| 0.9013 | 1050 | 0.0003 | - |
| 0.9442 | 1100 | 0.0004 | - |
| 0.9871 | 1150 | 0.0003 | - |
| 1.0300 | 1200 | 0.0003 | - |
| 1.0730 | 1250 | 0.0004 | - |
| 1.1159 | 1300 | 0.0003 | - |
| 1.1588 | 1350 | 0.0005 | - |
| 1.2017 | 1400 | 0.0003 | - |
| 1.2446 | 1450 | 0.0003 | - |
| 1.2876 | 1500 | 0.0003 | - |
| 1.3305 | 1550 | 0.0003 | - |
| 1.3734 | 1600 | 0.0003 | - |
| 1.4163 | 1650 | 0.0003 | - |
| 1.4592 | 1700 | 0.0003 | - |
| 1.5021 | 1750 | 0.0005 | - |
| 1.5451 | 1800 | 0.0003 | - |
| 1.5880 | 1850 | 0.0003 | - |
| 1.6309 | 1900 | 0.0003 | - |
| 1.6738 | 1950 | 0.0005 | - |
| 1.7167 | 2000 | 0.0003 | - |
| 1.7597 | 2050 | 0.0007 | - |
| 1.8026 | 2100 | 0.0003 | - |
| 1.8455 | 2150 | 0.0003 | - |
| 1.8884 | 2200 | 0.0003 | - |
| 1.9313 | 2250 | 0.0003 | - |
| 1.9742 | 2300 | 0.0003 | - |
| 2.0172 | 2350 | 0.0003 | - |
| 2.0601 | 2400 | 0.0003 | - |
| 2.1030 | 2450 | 0.0003 | - |
| 2.1459 | 2500 | 0.0003 | - |
| 2.1888 | 2550 | 0.0002 | - |
| 2.2318 | 2600 | 0.0003 | - |
| 2.2747 | 2650 | 0.0004 | - |
| 2.3176 | 2700 | 0.0002 | - |
| 2.3605 | 2750 | 0.0003 | - |
| 2.4034 | 2800 | 0.0002 | - |
| 2.4464 | 2850 | 0.0002 | - |
| 2.4893 | 2900 | 0.0002 | - |
| 2.5322 | 2950 | 0.0002 | - |
| 2.5751 | 3000 | 0.0002 | - |
| 2.6180 | 3050 | 0.0004 | - |
| 2.6609 | 3100 | 0.0004 | - |
| 2.7039 | 3150 | 0.0003 | - |
| 2.7468 | 3200 | 0.0003 | - |
| 2.7897 | 3250 | 0.0003 | - |
| 2.8326 | 3300 | 0.0003 | - |
| 2.8755 | 3350 | 0.0003 | - |
| 2.9185 | 3400 | 0.0003 | - |
| 2.9614 | 3450 | 0.0005 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.1
- PyTorch: 2.1.0+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## 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}
}
```