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metadata
base_model: BAAI/bge-base-en-v1.5
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      The provided answer is quite disjointed and does not directly address the
      specific question about accessing the company's training resources. The
      listed methods are more related to accessing various internal tools and
      processes rather than directly answering the question. Let's break down
      the problems:


      1. **System and personal documents**: Mentions accessing personal
      documents, contracts, and making reimbursements, which are not directly
      related to training resources.

      2. **Password Manager (1Password)**: This piece of information about
      managing passwords is irrelevant to accessing training resources.

      3. **Tresorit**: Focuses on secure information sharing, not on training
      resources.

      4. **Coffee session for feedback**: This is related to clarifying
      feedback, not directly about accessing training resources.

      5. **Learning Budget**: This section is somewhat relevant but is more
      about requesting financial support for training rather than providing a
      direct method to access training resources.


      The answer needs to clearly outline specific steps or platforms dedicated
      to training resources, which is mostly missing here.


      Final Result: **Bad**
  - text: >-
      The answer succinctly addresses the question by stating that
      finance@ORGANIZATION_2.<89312988> should be contacted for questions about
      travel reimbursement. This is correctly derived from the provided
      document, which specifies that questions about travel costs and
      reimbursements should be directed to the finance email.


      Final evaluation: Good
  - text: >-
      The answer provided correctly mentions the essential aspects outlined in
      the document, such as the importance for team leads to actively consider
      the possibility of team members leaving, to flag these situations to HR,
      analyze problems, provide feedback, and take proactive steps in various
      issues like underperformance or lack of growth. The answer also captures
      the significance of creating a supportive environment, maintaining
      alignment with the company's vision and mission, ensuring work-life
      balance, and providing regular feedback and praise.


      However, while the answer is generally comprehensive, it could be slightly
      more direct and concise in its communication. The document points to the
      necessity for team leads to think about potential exits to preemptively
      address issues and essentially prevent situations that may necessitate
      separation if they aren't managed well. This could have been emphasized
      more clearly.


      Overall, the answer aligns well with the content and intent of the
      document.


      Final evaluation: Good
  - text: >-
      Reasoning:

      The answer provided is relevant to the question as it directs the user to
      go to the website and check job ads and newsletters for more information
      about ORGANIZATION. However, it lacks comprehensive details. It only
      partially addresses how one can understand ORGANIZATION's product,
      challenges, and future since the documents suggest accessing job ads and
      newsletters, and no further content or documents were leveraged to provide
      insights into product details, current challenges, or future plans.


      Final evaluation: Bad
  - text: >-
      Evaluation:

      The answer provides a detailed response directly addressing the question,
      mentioning that the ORGANIZATION_2 and key individuals like Thomas Barnes
      and Charlotte Herrera play a supportive role in the farewell process. This
      includes handling paperwork, providing guidance, and assisting with tough
      conversations. The answer aligns well with the details provided in the
      document.


      The final evaluation: Good
inference: true
model-index:
  - name: SetFit with BAAI/bge-base-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.6268656716417911
            name: Accuracy

SetFit with BAAI/bge-base-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-base-en-v1.5 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:

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

Model Details

Model Description

Model Sources

Model Labels

Label Examples
0
  • 'The answer provided is a general approach to saving money, offering advice on spending wisely, making good investments, and considering personal goals. It also suggests discussing with a lead or ORGANIZATION_2 for guidance and highlights the importance of health and setting priorities.\n\nHowever, the answer lacks direct grounding in the provided document, which primarily discusses budgeting for studies and spending on learning and development. The document emphasizes evaluating expenditures based on their benefits to personal and organizational goals but does not offer explicit general financial saving tips.\n\nDue to this lack of specific correlation and grounding in the document, the evaluation is:\n\nBad'
  • 'The answer provided reads incoherently due to the insertion of "Cassandra Rivera Heather Nelson" and other names in inappropriate places throughout the text, making it difficult to assess its alignment with the original document.\n\nFurthermore, the inserted names disrupt the meaning of the text, making it unclear if all relevant points from the document are covered accurately. The structure of the sentences becomes disjointed and presents an overall lack of clarity. \n\nGiven the nature of these errors, it's impossible to fairly evaluate whether the answer strictly adheres to the details provided in the source documents. Therefore, based on the clarity, coherence, and alignment to the original text, the final result is:\n\nBad'
  • "The provided answer does not satisfactorily respond to the question about accessing the company's training resources. Instead, it contains unrelated information about document management, security protocols, feedback processes, and learning budget requests. The relevant information about accessing training resources is clearly missing or obscure.\n\n**Reasoning:**\n\n1. Irrelevant Details: The answer includes details about the usage of a password manager, secure sharing of information, and expense reimbursement, none of which pertain to accessing training resources.\n\n2. Lack of Specificity: No explicit method or platform for accessing training resources is mentioned, which is the core inquiry.\n\n3. Missed Key Point: The document points towards systems used for personal documents and reimbursement requests but fails to highlight training resource access points.\n\nFinal evaluation: Bad"
1
  • 'The answer demonstrates a clear connection to the provided document, outlining key tips and principles for giving feedback as requested by the question. The response includes the importance of timing, focusing on the situation rather than the person, being clear and direct, and the goal of helping rather than shaming. It also mentions the importance of appreciation and receiving feedback with an open mind. \n\nHowever, there are some typographical errors and misplaced words (e.g., "emichelle James Johnson MDamples") that detract slightly from the clarity. Despite these minor issues, the content provided accurately reflects the information in the source document and comprehensively addresses the question. Therefore, the final evaluation is:\n\nGood'
  • "The given answer provides a general explanation of why it is important to proactively share information from high-level meetings, but it lacks grounding in the provided document. \n\nWhile the answer discusses the benefits of transparency, alignment with the organization's vision and mission, and fostering an open work environment, it does not directly quote or refer to specific points in the document. This weakens the argument, as it seems more like an independent explanation rather than an answer strictly based on the provided material.\n\nThe document mentions the importance of employees knowing why they are doing what they do and the necessity of closing the information gap by proactively sharing what was discussed in high-level meetings. This specific point from Document 4 could have been directly referenced to make the answer more aligned with the source material.\n\nThus, the answer, although conceptually correct, does not appropriately leverage the provided document.\n\nFinal result: Bad"
  • "The answer is partially correct since it provides the most important guidance on how to report car travel expenses for reimbursement. However, it contains inaccuracies and omissions that could cause confusion. \n\n1. The email addresses cited in the answer don’t match those in the document.\n2. There’s mention of requesting a parking card for a specific date (2004-04-14), which implies an inaccurate, irrelevant detail that might mislead the reader.\n3. The answer doesn't explicitly suggest that travel cost reimbursement is handled monthly, which is a crucial piece of information.\n\nConsidering these elements, the evaluation is as follows:\n\n**Evaluation:**\nThe essential details related to car travel reimbursement are present, but incorrect email addresses and irrelevant details might mislead or cause inconvenience for employees.\n\nThe final evaluation: Bad"

Evaluation

Metrics

Label Accuracy
all 0.6269

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("Netta1994/setfit_baai_newrelic_gpt-4o_cot-few_shot_only_reasoning_1726750220.456809")
# Run inference
preds = model("The answer succinctly addresses the question by stating that finance@ORGANIZATION_2.<89312988> should be contacted for questions about travel reimbursement. This is correctly derived from the provided document, which specifies that questions about travel costs and reimbursements should be directed to the finance email.

Final evaluation: Good")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 30 85.7538 210
Label Training Sample Count
0 32
1 33

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (5, 5)
  • 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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0061 1 0.2304 -
0.3067 50 0.2556 -
0.6135 100 0.244 -
0.9202 150 0.1218 -
1.2270 200 0.0041 -
1.5337 250 0.0022 -
1.8405 300 0.0017 -
2.1472 350 0.0017 -
2.4540 400 0.0015 -
2.7607 450 0.0014 -
3.0675 500 0.0013 -
3.3742 550 0.0013 -
3.6810 600 0.0012 -
3.9877 650 0.0012 -
4.2945 700 0.0012 -
4.6012 750 0.0012 -
4.9080 800 0.0012 -

Framework Versions

  • Python: 3.10.14
  • SetFit: 1.1.0
  • Sentence Transformers: 3.1.0
  • Transformers: 4.44.0
  • PyTorch: 2.4.1+cu121
  • Datasets: 2.19.2
  • Tokenizers: 0.19.1

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}
}