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sijan1/
empathy_model2

Name: empathy_model2
Rating: 2.5 (11 reviews)

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/all-MiniLM-L6-v2

finetune

model-index

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SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/all-MiniLM-L6-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 256 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label

Examples

"Jonathan, I hope you are well - I am very excited that you are part of this development team and really appreciate all the support you give to us; while doing this some comments have arise that can be opportunity areas to improve your work and get this program ahead.1. The communication between team members is not clear and improvements can be done to this: by this I mean to connect more with other team members before submitting your reports.2. One of the reasons you were chosen is because of your enthusiastic attitude and knowledge, but too much information sometimes can harm the delivery reports that needs to be concise and business oriented. 3.Please forward me your latest report so we can discuss it furthermore when I come back and see what can be improve and we can work from there.4. Please don't be discourage, these are opportunity areas that we can engage and as always keep up the good work. Have a great week. Thanks"
"Hi Jonathan, I hope this message finds you well. I hear things are going well with the Beta project. That said, Terry mentioned that there were some issues with the reports. From what I understand, they would like them to be more concise and straight to the point, as well as more business focused. I recommend you reach out to Terry so you both could review in detail one of the reports he submits. This should help you help you align to their expectations. Additionally, i'd be happy to review the reports before you send them off to Terry and provide my feedback. I know this project is important to you, so please let me know how this meeting goes and how else I can help. Regards, William"
'Hi Jonathan, Good to hear you are enjoying the work. I would like to discuss with you feedback on your assignment and the reports you are producing. It is very important to understand the stakeholders who will be reading your report. You may have gathered a lot of good information BUT do not put them all on your reports. The report should state facts and not your opinions. Create reports for the purpose and for the audience. I would also suggest that you reach out to Terry to understand what information is needed on the reports you produce.Having said that, the additional insights you gathered are very important too. Please add them to our knowledge repository and share with the team. It will be a great sharing and learning experience. You are very valuable in your knowledge and I think that it would benefit you and the organization tremendously when you are to channelize your insights and present the facts well. I would encourage you to enroll for the business writing training course. Please choose a date from the learning calendar and let me know. Regards, William'

'Good Afternoon Jonathan, I hope you are well and the travelling is not too exhausting. I wanted to touch base with you to see how you are enjoying working with the Beta project team? I have been advised that you are a great contributor and are identifying some great improvements, so well done. I understand you are completing a lot of reports and imagine this is quite time consuming which added to your traveling must be quite overwhelming. I have reviewed some of your reports and whilst they provide all the technical information that is required, they are quite lengthy and i think it would be beneficial for you to have some training on report structures. This would mean you could spend less time on the reports by providing only the main facts needed and perhaps take on more responsibility. When the reports are reviewed by higher management they need to be able to clearly and quickly identify any issues. Attending some training would also be great to add to your career profile for the future. In the meantime perhaps you could review your reports before submitting to ensure they are clear and consise with only the technical information needed,Let me know your thoughts. Many thanks again and well done for all your hard work. Kind regards William'
'Jonathan, First I want to thank you for your help with the Beta project. However, it has been brought to my attention that perhaps ABC-5 didn't do enough to prepare you for the extra work and I would like to discuss some issues. The nature of these reports requires them to be technical in nature. Your insights are very valuable and much appreciated but as the old line goes "please give me just the facts". Given the critical nature of the information you are providing I can't stress the importance of concise yet detail factual reports. I would like to review your reports as a training exercise to help you better meet the team requirements. Given that there are some major reports coming up in the immediate future, I would like you to review some training options and then present a report for review. Again your insights are appreciated but we need to make sure we are presenting the end-use with only the information they need to make a sound business decision. I also understand you would like to grow into a leadership position so I would like to discuss how successfully implementing these changes would be beneficial in demonstrating an ability to grow and take on new challenges. '
'Hi Jonathan, I wanted to have a discussion with you but since you are travelling i am sharing in this mailThis is related to Beta project and reports coming from there.While we are all excited by the passion and enthusiasm you are bringing i wanted to share some early feedback with you. 1.Please try to be concise in reports and mention facts that teams can refer . We love opinions but lets save those for our brainstorming discussions. 2.For Business writing as you are getting started to help you set up for success we are nominating you for a training program so that your reports are way more effective. I hope as you set on your growth journey and take larger roles a superb feedback from your peers and stakeholders will help. I truly believe above two points can really help you take you there. Wishing you all the best and do share in case you have feedback or inputs from your side. Regards William'

Evaluation

Metrics

Label	Accuracy
all	0.5909

Uses

Direct Use for Inference

First install the SetFit library:

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("sijan1/empathy_model2")
# Run inference
preds = model("Hi Jonathan, and I hope your travels are going well. As soon as you get a chance, I would like to catch up on the reports you are creating for the Beta projects.  Your contributions have been fantastic, but we need to limit the commentary and make them more concise.  I would love to get your perspective and show you an example as well.  Our goal is to continue to make you better at what you do and to deliver an excellent customer experience.  Looking forward to tackling this together and to your dedication to being great at what you do. Safe travels and I look forward to your call.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	129	199.5	308

Label	Training Sample Count
0	4
1	4

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
0.05	1	0.238	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.5.0
Transformers: 4.37.2
PyTorch: 2.1.0+cu121
Datasets: 2.17.1
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}
}