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dim1_setfit_model

Text Classification
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 ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Thesis: In my opinion, watching sports on TV is a good opportunity to get
relax. There are many reasons why some people like to watch sport games on TV.
Last argument: None Target sentence: Where is the truth?'
- text: 'Thesis: As for me, I suppose that watching sports via TV won''t help you
become a professional at sport. Moreover, sport events are not very interesting
to be seen through television channels. Last argument: Besides, it''s much more
attractive to visit the play in the real life than to watch it at home for many
reasons. Target sentence: If you watch the sport programme, you can see only those
things that the operator wants to record.'
- text: 'Thesis: Today there are people who belived that watching any sport is a useless
time spent. I complitely disagree with this opinion. Last argument: Watching any
sort games or individual competition is wonderfull way to spend your free time,
by this hobby you can have a lot of profits. Target sentence: Watching any sort
games or individual competition is wonderfull way to spend your free time, by
this hobby you can have a lot of profits.'
- text: 'Thesis: As for me, I suppose that watching sports via TV won''t help you
become a professional at sport. Moreover, sport events are not very interesting
to be seen through television channels. Last argument: Besides, it''s much more
attractive to visit the play in the real life than to watch it at home for many
reasons. Target sentence: Besides, it''s much more attractive to visit the play
in the real life than to watch it at home for many reasons.'
- text: 'Thesis: Some people consider that it is a waste of time, bit I desagree with
this statment and try to refute it. Last argument: If looks on this statement
otherwise, it is importnat to say that watching sport events is a usual hobby
such as cooking, reading books and others. Target sentence: People who work in
public catering like cooking and it is their hobby maybe.'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
datasets:
- Zlovoblachko/DeepSeek_dim1
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Zlovoblachko/DeepSeek_dim1
type: Zlovoblachko/DeepSeek_dim1
split: test
metrics:
- type: accuracy
value: 0.8740740740740741
name: Accuracy
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Zlovoblachko/DeepSeek_dim1](https://huggingface.co/datasets/Zlovoblachko/DeepSeek_dim1) dataset 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.
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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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
- **Training Dataset:** [Zlovoblachko/DeepSeek_dim1](https://huggingface.co/datasets/Zlovoblachko/DeepSeek_dim1)
<!-- - **Language:** Unknown -->
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### 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 |
|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| L | <ul><li>'Thesis: in my opinion it is uself and fun to do pysical exercise and my activity every day. I disagre wits his opinion because these people do not understand that sport should keep fit and mind. Last argument: Secondly the sport is very fun. Target sentence: However some people prefer to watch sports show on tv.'</li><li>'Thesis: I personally disadree with this opinion because there are many reasons why watching sports can be useful for people. Last argument: However, I can understend people who agree with first point of view. Target sentence: On the other hand, sometimes it is very difficult to control the time which they lose for that.'</li><li>"Thesis: None Last argument: People opposing this position may say that sport is not an intellectual activity, so it is not worth spending time at all. Target sentence: They think that watching sport is not developing people's mind, so it has no sense to watch such things."</li></ul> |
| H | <ul><li>'Thesis: I think, that watching sports really gives a lot of fun. Last argument: The second point is that sports fans are fond of their teams ond sports favoritres. Target sentence: Sports stimulates them to travel (like UFC or Olympic games), to collect merch, to have new datings.'</li><li>'Thesis: So, can watching sport be called a waste of time. To my mind, observing a sports game is a fascinating pastime. However, in my opinion they are mistaken. Last argument: Even some hidden talents can be discovered. Target sentence: Although, it is obvious that we should not spend much time in front of the TV, some people believe that watching sports can make a person obese.'</li><li>'Thesis: Some people think that spending free time watching sport on TV is just killing precious time. However, my personal opinion is that this activity is useful. Last argument: Also, it can be a good practice if you play some sport Target sentence: because while watching game you can learn some tricks of this game and then apply them in life.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8741 |
## 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("Zlovoblachko/dim1_setfit_model")
# Run inference
preds = model("Thesis: In my opinion, watching sports on TV is a good opportunity to get relax. There are many reasons why some people like to watch sport games on TV. Last argument: None Target sentence: Where is the truth?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 13 | 62.7806 | 140 |
| Label | Training Sample Count |
|:------|:----------------------|
| L | 540 |
| H | 540 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2674 | - |
| 0.0014 | 50 | 0.3254 | - |
| 0.0027 | 100 | 0.3135 | 0.3191 |
| 0.0041 | 150 | 0.2963 | - |
| 0.0055 | 200 | 0.2799 | 0.2615 |
| 0.0068 | 250 | 0.2521 | - |
| 0.0082 | 300 | 0.2554 | 0.2495 |
| 0.0096 | 350 | 0.2518 | - |
| 0.0110 | 400 | 0.2484 | 0.2488 |
| 0.0123 | 450 | 0.2498 | - |
| 0.0137 | 500 | 0.2456 | 0.2465 |
| 0.0151 | 550 | 0.2449 | - |
| 0.0164 | 600 | 0.2433 | 0.2435 |
| 0.0178 | 650 | 0.2416 | - |
| 0.0192 | 700 | 0.2424 | 0.2410 |
| 0.0205 | 750 | 0.2381 | - |
| 0.0219 | 800 | 0.2302 | 0.2300 |
| 0.0233 | 850 | 0.227 | - |
| 0.0246 | 900 | 0.2222 | 0.2428 |
| 0.0260 | 950 | 0.2129 | - |
| 0.0274 | 1000 | 0.2138 | 0.2144 |
| 0.0288 | 1050 | 0.2026 | - |
| 0.0301 | 1100 | 0.1888 | 0.2009 |
| 0.0315 | 1150 | 0.1735 | - |
| 0.0329 | 1200 | 0.1658 | 0.2017 |
| 0.0342 | 1250 | 0.1646 | - |
| 0.0356 | 1300 | 0.1442 | 0.1889 |
| 0.0370 | 1350 | 0.1428 | - |
| 0.0383 | 1400 | 0.1169 | 0.1804 |
| 0.0397 | 1450 | 0.1237 | - |
| 0.0411 | 1500 | 0.0989 | 0.1838 |
| 0.0424 | 1550 | 0.106 | - |
| 0.0438 | 1600 | 0.102 | 0.1703 |
| 0.0452 | 1650 | 0.0823 | - |
| 0.0466 | 1700 | 0.0822 | 0.1786 |
| 0.0479 | 1750 | 0.081 | - |
| 0.0493 | 1800 | 0.0674 | 0.1685 |
| 0.0507 | 1850 | 0.0593 | - |
| 0.0520 | 1900 | 0.0659 | 0.1732 |
| 0.0534 | 1950 | 0.0546 | - |
| 0.0548 | 2000 | 0.0508 | 0.1889 |
| 0.0561 | 2050 | 0.0447 | - |
| 0.0575 | 2100 | 0.0462 | 0.1637 |
| 0.0589 | 2150 | 0.0348 | - |
| 0.0602 | 2200 | 0.0256 | 0.2151 |
| 0.0616 | 2250 | 0.0273 | - |
| 0.0630 | 2300 | 0.0183 | 0.2285 |
| 0.0644 | 2350 | 0.0194 | - |
| 0.0657 | 2400 | 0.0245 | 0.2068 |
### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.3
- Sentence Transformers: 4.1.0
- Transformers: 4.54.0
- PyTorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}
}
```
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