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harris

Text Classification
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 ---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: I felt happy and content last night. I was with my husband and daughter and
we had just had dinner. We were hanging out, watching tv, eating cookies and playing
games. It was amazing!
- text: 'I felt a positive emotion when I visited my friend last weekend. We had a
great conversation about our feelings, hopes, and aspirations. I felt present,
connected, and loved by someone else. '
- text: 'I feel positive when interacting with my children. They can be a source
of frustration, but they are more often a source of pride and joy. Whenever they
achieve something, whether it be in sports or school, I cannot explain how bursting
with pride I get. Once you have children, your whole life changes, and emotions
both good and bad are centered around them. '
- text: I was able to cut my taxes in half. Also, our homeowners insurance was reduced
by almost 1k and we are now receiving more coverage. Additionally, I managed to
get our mortgage reduced from $2700 to $603.37. Quite proud of my effort(s) and
the results. :)
- text: When I make a good sale at work it makes me feel so good. Also having a good
experience with my customers and them being happy with their purchase. It makes
me feel very good about my job.
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.4772727272727273
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **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:** 9 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **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 |
|:------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| business and completing tasks | <ul><li>'I am feeling positive today that I am going to complete as much work as I can to ensure that I can go to work tomorrow, barring exhaustion. I am also excited for the upcoming storm. Storms bring a sense of positivity.'</li><li>'When I first started my online store selling books. I thought positive I am going to sell tons of books this is going to be easy work and I am going to make thousands. lol I believed in myself a lot more then.'</li></ul> |
| connecting with others | <ul><li>"One time that I felt a positive emotion was last week. I was able to see my entire extended family on thanksgiving at my grandmother's house. I just felt overjoyed and filled with love. We haven't had everyone together since before COVID, so it felt great to be around fun, family, friends, and food."</li><li>"I felt a positive emotion recently when I was at a friend's wedding. During the ceremony, I felt strong emotions of happiness, pride, and love. I felt these emotions because it was so powerful seeing my friends of many years getting married, and hearing them express their love to each other."</li></ul> |
| dreams and goals | <ul><li>'I feel position when I accomplish a goal or make progress on a goal that I have set for myself. For example, I have a daily goal of walking five miles. If I walk around four or more miles, I feel positive about my day. If I walk more than five miles, I feel even more positive about my accomplishments. '</li><li>'One time, I felt an overwhelming sense of joy, contentment, and gratitude when I was accepted into my dream university. This positive emotion arose from the realization of achieving a long-held goal and the validation of my hard work paying off. I felt an immense sense of pride and excitement about the opportunities that lay ahead, and it motivated me to embark on a new chapter in my life with enthusiasm and determination.'</li></ul> |
| engaging with hobbies and accomplishments | <ul><li>"Well, this may not be what you're looking for, but I've been feeling happy and enthusiastic about building a new desktop computer. I've ordered the parts and every time one of them comes in, I'm that much closer to the goal. The anticipation isn't really an emotion I suppose, but it is a really positive feeling for me."</li><li>'I just felt so excited that I managed to make two fingerless gloves on my knitting looms for the first time. They look and feel great and my mom is going to love knowing I was thinking of her. '</li></ul> |
| overcoming challenges | <ul><li>'I was able to cut my taxes in half. Also, our homeowners insurance was reduced by almost 1k and we are now receiving more coverage. Additionally, I managed to get our mortgage reduced from $2700 to $603.37. Quite proud of my effort(s) and the results. :)'</li><li>'I was able to cut my taxes in half. Also, our homeowners insurance was reduced by almost 1k and we are now receiving more coverage. Additionally, I managed to get our mortgage reduced from $2700 to $603.37. Quite proud of my effort(s) and the results. :)'</li></ul> |
| parenthood, taking care of something | <ul><li>"This morning I was snuggling with my 9-year-old son. For a few minutes I really looked at his face, at how he's getting older, but how much I still love him. I felt grateful that I have him, a lot of love, and at peace."</li><li>'I felt a positive emotion at the birth of my daughter. I was almost 50 at the time and after raising two sons, I knew I was entering, very possibly, a new enlightening and respectful period in my life. As time has passed since then, I have found that love has truly entered my life as never expected.'</li></ul> |
| professional and academic accomplishments | <ul><li>'I felt a positive emotion when I got promoted as a manager of my firm. I worked really hard to attain this goal. My emotions went out of control when I took charge as a manager of my firm.'</li><li>'I felt a surge of confidence and competence when I got my first real job. This real job was based on my hard work at school and was a career job that paid well. I felt my life making a turn to the good and that I could finally relax and feel some energy and peace that I could count on to last a long time.'</li></ul> |
| quality time and vacations | <ul><li>'I felt a positive emotion when I was on vacation in Hawaii. When i sit on the beach and stare out at the ocean I have a sense of calm and I feel postivie about all the world. I feel in awe of the world and the vast ocean. '</li><li>'I felt a positive emotion when I went to visit NYC recently because I love that city. I find the city to be very exciting and motivating so it brings out many positive emotions in me when I am there.'</li></ul> |
| simple joys | <ul><li>"I last felt a positive emotion this morning. I go outside every morning into my backyard with my cats, and I watched my cat chase birds unsuccessfully for a few minutes, which had me laughing. He's so cute when he does that, it made my morning."</li><li>"Thankfulness emerges when we recognize that someone or something is a positive in our life. We might feel gratitude for gifts we've received, kindnesses extended to us, or for something as simple as being able to wake up each day."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.4773 |
## 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("knharris4/harris")
# Run inference
preds = model("I felt happy and content last night. I was with my husband and daughter and we had just had dinner. We were hanging out, watching tv, eating cookies and playing games. It was amazing!")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 39 | 50.2222 | 73 |
| Label | Training Sample Count |
|:------------------------------------------|:----------------------|
| business and completing tasks | 2 |
| connecting with others | 2 |
| dreams and goals | 2 |
| engaging with hobbies and accomplishments | 2 |
| overcoming challenges | 2 |
| parenthood, taking care of something | 2 |
| professional and academic accomplishments | 2 |
| quality time and vacations | 2 |
| simple joys | 2 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 15
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0294 | 1 | 0.0416 | - |
| 1.4706 | 50 | 0.038 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Datasets: 3.0.2
- 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}
}
```
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