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:
- 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: BAAI/bge-base-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 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 | 0.6269 |
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}
}