--- 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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 256 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 | |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | | | 1 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.7692 | ## 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("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 ```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} } ```