metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
Hello Jonathan, Thank you for your work on the Beta project. I would like
for us to set up a meeting to discuss your work on the project. You have
completed a few reports now and I have had some feedback I would like to
share with you; specifically the commentary you are providing and your
business writing. The additional commentary you are providing makes it
difficult to find the objective facts of your findings while working with
a tight deadline. I would like to have a discussion with you what ideas
you may have to help make your reports more concise so the team can meet
their deadlines. You are investing considerable time and effort in these
reports and you have expressed your desire to be in an engineering role in
the future. Your work on these reports can certainly help you in achieving
your career goals. I want to make sure you are successful. I'll send out
a meeting invite shortly. Thank you again Jonathan for all your work on
this project. I'm looking forward to discussing this with you.
- text: >-
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
- text: >-
Hi Jonathan, I am glad to hear that you are enjoying your job, traveling
and learning more about the Beta ray technology. I wanted to share some
feedback with you that I received. I want to help you be able to advance
in your career and I feel that this feedback will be helpful. I am excited
that you are will to share your perspectives on the findings, however if
you could focus on the data portion first, and highlight the main points,
that would be really beneficial to your audience. By being more concise it
will allow the potential customers and then CEO to focus on the facts of
the report, which will allow them to make a decision for themselves. I
understand that this is probably a newer to writing the reports, and I
don't think that anyone has shown you an example of how the reports are
usually written, so I have sent you some examples for you to review. I
think that you are doing a good job learning and with this little tweak in
the report writing you will be able to advance in your career. In order to
help you, if you don't mind, I would like to review the report before you
submit it and then we can work together to ensure it will be a great
report. I understand that you really enjoy providing your perspectives on
the technology and recommendations on how it can be used, so we will find
a spot for that in the report as well, but perhaps in a different section.
Thank you so much for your time today and I look forward to working with
you.
- text: >-
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
- text: >-
Hi Jonathan, I understand you have been quite involved with the Beta
Project. Your experience is paying off as you are often finding
improvements the product team did not even know they needed. I wanted to
share some feedback I got from one of your colleagues regarding your
reports. Your enthusiasm for this project is infectious and I love to see
this level of engagement. However, we also want to be mindful of the end
users of the reports you are preparing. In these projects, deadlines often
move at a fast pace. In order to ensure the project can stay on time, it
is important to focus on inputting mainly facts when writing these
reports. You offer a unique perspective and your insights are greatly
appreciated. I would love to discuss your ideas with you in separate
meetings outside of this project. I understand you are having to compile
and organize a large amount of information. I appreciate how overwhelming
this can feel at times. When these reports are completed, they are
reviewed by our CEO and other key stakeholders. To ensure we are
respecting their time, we want these reports to by concise and well
organized. I would like you to set up some time with Terry to go over his
approach to these reports and his writing style. Once I am back from
assignment I will set up time to review how this meeting went and discuss
other ideas you may have. I greatly appreciate your efforts on this
project and positive attitude. With the above mentioned areas of
opportunity, I know this project will continue to run smoothly. Thanks.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.7692307692307693
name: Accuracy
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 |
|---|---|
| 0 |
|
| 1 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.7692 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("sijan1/empathy_model")
# Run inference
preds = model("Hello Jonathan, Thank you for your work on the Beta project. I would like for us to set up a meeting to discuss your work on the project. You have completed a few reports now and I have had some feedback I would like to share with you; specifically the commentary you are providing and your business writing. The additional commentary you are providing makes it difficult to find the objective facts of your findings while working with a tight deadline. I would like to have a discussion with you what ideas you may have to help make your reports more concise so the team can meet their deadlines. You are investing considerable time and effort in these reports and you have expressed your desire to be in an engineering role in the future. Your work on these reports can certainly help you in achieving your career goals. I want to make sure you are successful. I'll send out a meeting invite shortly. Thank you again Jonathan for all your work on this project. I'm looking forward to discussing this with you.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 114 | 187.5 | 338 |
| Label | Training Sample Count |
|---|---|
| 0 | 2 |
| 1 | 2 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- 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 |
|---|---|---|---|
| 0.025 | 1 | 0.0001 | - |
| 2.5 | 50 | 0.0001 | - |
| 0.0667 | 1 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.2
Citation
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}
}