---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Federal Office for Radiation Protection established a new monitoring network
around nuclear waste storage sites.
- text: how could we imagine these mechanisms to be implemented within a nato-based
missile defence system.
- text: president putin said that the precondition for a ceasefire is that ukraine
should give up even more land, to give up all the four provinces that russia has
annexed without controlling.
- text: and helped protect and defend turkey’s territory and citizens against missile
attacks.
- text: let me first of all say that we take nuclear issues extremely seriously.
metrics:
- f1_macro
- f1_binary
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: codefuse-ai/F2LLM-v2-80M
model-index:
- name: SetFit with codefuse-ai/F2LLM-v2-80M
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1_macro
value: 0.9169073916737468
name: F1_Macro
- type: f1_binary
value: 0.9065420560747663
name: F1_Binary
---
# SetFit with codefuse-ai/F2LLM-v2-80M
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [codefuse-ai/F2LLM-v2-80M](https://huggingface.co/codefuse-ai/F2LLM-v2-80M) 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:** [codefuse-ai/F2LLM-v2-80M](https://huggingface.co/codefuse-ai/F2LLM-v2-80M)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 40960 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 |
|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | - 'actually, we have decided in nato at the last summit that we should explore the possibilities to integrate russian missile defence systems in our missile defence system, which i think has become even more easy after the u.s. has presented new missile defence plans.'
- 'and we see, of course, the risk of proliferation of nuclear weapons.'
- 'there are of course opportunities and we need to engage with china on issues like climate change, arms control.'
|
| negative | - 'We welcome the successful achievement of a draft Chemical Weapons Convention.'
- 'in practice, this means that, in addition to reinforcing cooperation with our current partners, we should look to enhance our relations with countries such as australia , new zealand , japan and south korea.'
- 'this is not about militarizing space.'
|
## Evaluation
### Metrics
| Label | F1_Macro | F1_Binary |
|:--------|:---------|:----------|
| **all** | 0.9169 | 0.9065 |
## 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("fefofico/nuclear_trained_f2llm_temp")
# Run inference
preds = model("let me first of all say that we take nuclear issues extremely seriously.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 23.7926 | 132 |
| Label | Training Sample Count |
|:---------|:----------------------|
| negative | 1096 |
| positive | 857 |
### Training Hyperparameters
- batch_size: (128, 128)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (5e-07, 5e-07)
- head_learning_rate: 0.0002
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.35
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0016 | 1 | 0.3497 | - |
| 0.0327 | 20 | 0.3955 | - |
| 0.0655 | 40 | 0.3678 | - |
| 0.0982 | 60 | 0.3743 | - |
| 0.1309 | 80 | 0.3384 | - |
| 0.1637 | 100 | 0.3244 | - |
| 0.1964 | 120 | 0.3087 | - |
| 0.2291 | 140 | 0.29 | - |
| 0.2619 | 160 | 0.2675 | - |
| 0.2946 | 180 | 0.2659 | - |
| 0.3273 | 200 | 0.2584 | - |
| 0.3601 | 220 | 0.2573 | - |
| 0.3928 | 240 | 0.2544 | - |
| 0.4255 | 260 | 0.2541 | - |
| 0.4583 | 280 | 0.2498 | - |
| 0.4910 | 300 | 0.2492 | - |
| 0.5237 | 320 | 0.2471 | - |
| 0.5565 | 340 | 0.2397 | - |
| 0.5892 | 360 | 0.2356 | - |
| 0.6219 | 380 | 0.2287 | - |
| 0.6547 | 400 | 0.2277 | - |
| 0.6874 | 420 | 0.223 | - |
| 0.7201 | 440 | 0.2169 | - |
| 0.7529 | 460 | 0.2154 | - |
| 0.7856 | 480 | 0.2067 | - |
| 0.8183 | 500 | 0.2084 | - |
| 0.8511 | 520 | 0.1983 | - |
| 0.8838 | 540 | 0.199 | - |
| 0.9165 | 560 | 0.1999 | - |
| 0.9493 | 580 | 0.1939 | - |
| 0.9820 | 600 | 0.1909 | - |
| 1.0 | 611 | - | 0.1728 |
| 1.0147 | 620 | 0.1827 | - |
| 1.0475 | 640 | 0.1736 | - |
| 1.0802 | 660 | 0.1788 | - |
| 1.1129 | 680 | 0.1741 | - |
| 1.1457 | 700 | 0.1731 | - |
| 1.1784 | 720 | 0.1734 | - |
| 1.2111 | 740 | 0.1645 | - |
| 1.2439 | 760 | 0.1679 | - |
| 1.2766 | 780 | 0.1602 | - |
| 1.3093 | 800 | 0.1525 | - |
| 1.3421 | 820 | 0.1519 | - |
| 1.3748 | 840 | 0.1563 | - |
| 1.4075 | 860 | 0.1564 | - |
| 1.4403 | 880 | 0.1502 | - |
| 1.4730 | 900 | 0.144 | - |
| 1.5057 | 920 | 0.1479 | - |
| 1.5385 | 940 | 0.1472 | - |
| 1.5712 | 960 | 0.1461 | - |
| 1.6039 | 980 | 0.137 | - |
| 1.6367 | 1000 | 0.1497 | - |
| 1.6694 | 1020 | 0.1433 | - |
| 1.7021 | 1040 | 0.1426 | - |
| 1.7349 | 1060 | 0.1345 | - |
| 1.7676 | 1080 | 0.1406 | - |
| 1.8003 | 1100 | 0.135 | - |
| 1.8331 | 1120 | 0.1434 | - |
| 1.8658 | 1140 | 0.1407 | - |
| 1.8985 | 1160 | 0.1353 | - |
| 1.9313 | 1180 | 0.133 | - |
| 1.9640 | 1200 | 0.1326 | - |
| 1.9967 | 1220 | 0.1357 | - |
| 2.0 | 1222 | - | 0.1313 |
| 0.0016 | 1 | 0.1361 | - |
| 0.0327 | 20 | 0.1349 | - |
| 0.0655 | 40 | 0.1338 | - |
| 0.0982 | 60 | 0.1338 | - |
| 0.1309 | 80 | 0.1412 | - |
| 0.1637 | 100 | 0.1269 | - |
| 0.1964 | 120 | 0.1213 | - |
| 0.2291 | 140 | 0.1266 | - |
| 0.2619 | 160 | 0.1239 | - |
| 0.2946 | 180 | 0.1162 | - |
| 0.3273 | 200 | 0.1121 | - |
| 0.3601 | 220 | 0.1136 | - |
| 0.3928 | 240 | 0.111 | - |
| 0.4255 | 260 | 0.11 | - |
| 0.4583 | 280 | 0.1091 | - |
| 0.4910 | 300 | 0.1009 | - |
| 0.5237 | 320 | 0.0963 | - |
| 0.5565 | 340 | 0.094 | - |
| 0.5892 | 360 | 0.1001 | - |
| 0.6219 | 380 | 0.0956 | - |
| 0.6547 | 400 | 0.0889 | - |
| 0.6874 | 420 | 0.0895 | - |
| 0.7201 | 440 | 0.0934 | - |
| 0.7529 | 460 | 0.0857 | - |
| 0.7856 | 480 | 0.0882 | - |
| 0.8183 | 500 | 0.0878 | - |
| 0.8511 | 520 | 0.0878 | - |
| 0.8838 | 540 | 0.0909 | - |
| 0.9165 | 560 | 0.0928 | - |
| 0.9493 | 580 | 0.0903 | - |
| 0.9820 | 600 | 0.0925 | - |
| 1.0 | 611 | - | 0.1090 |
### Framework Versions
- Python: 3.12.13
- SetFit: 1.1.3
- Sentence Transformers: 3.4.1
- Transformers: 4.57.6
- PyTorch: 2.11.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2
## 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}
}
```