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Netta1994/
setfit_baai_gpt-4o_improved-cot-instructions_chat_few_shot_generated_remove_final_eva

Name: setfit_baai_gpt-4o_improved-cot-instructions_chat_few_shot_generated_remove_final_eva
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

BAAI/bge-base-en-v1.5

finetune

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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

"Reasoning:\nThe answer provides key insights into the reasons behind the Denver Nuggets' offensive outburst in January, specifically mentioning the comfort and effectiveness of the team, the coaching strategy of taking the first available shot in the rhythm of the offense, and emphasizing pushing the ball after both makes and misses. These points are directly supported by the document. However, the inclusion of information about a new training technique involving virtual reality is not supported by the provided document and thus detracts from the answer's accuracy and relevance.\n\nFinal Evaluation:"
'Reasoning:\nWhile the provided answer attempts to address the differences between film and digital photography, it contains several inaccuracies and inconsistencies with the document. The answer incorrectly states that film under-exposes better and compresses the range into the bottom end, whereas the document clearly states that film over-exposes better and compresses the range into the top end. Additionally, it mentions that the digital sensors capture all three colors at each point, which is inaccurate as per the document; it states digital sensors capture only one color at each point and then interpolate the data to create an RGB image. Moreover, the answer states the comparison is to 5MP digital sensors whereas the document talks about 10MP sensors.\n\nThese inaccuracies undermine the grounding andreliability of the answer.\n\nFinal Evaluation:'
"Reasoning:\nThe provided answer addresses a topic entirely unrelated to the given question. The question is about the main conflict in the third book of the Arcana Chronicles by Kresley Cole, but the answer discusses the result of an MMA event featuring Antonio Rogerio Nogueira. There is no connection between the document's content and the question, leading to a clear lack of context grounding, relevance, and conciseness.\n\nFinal Evaluation: \nEvaluation:"

'Reasoning:\nThe answer provided effectively draws upon the document to list several best practices that a web designer can incorporate into their client discovery and web design process to avoid unnecessary revisions and conflicts. Each practice mentioned—getting to know the client and their needs, signing a detailed contract, and communicating honestly about extra charges—is directly supported by points raised in the document. The answer is relevant, concise, and closely aligned with the specific question asked.\n\nFinal Evaluation:'
"Reasoning:\nThe answer provided correctly identifies the author's belief on what creates a connection between the reader and the characters in a story. It states that drawing from the author's own painful and emotional experiences makes the story genuine and relatable, thus engaging the reader. This is consistent with the document, which emphasizes the importance of authenticity and emotional depth derived from the author's personal experiences to make characters and their struggles real to the reader. The answer is relevant to the question and concisely captures the essence of the author's argument without includingunnecessary information.\n\nFinal Evaluation:"
'Reasoning:\nThe answer correctly identifies Mauro Rubin as the CEO of JoinPad during the event at Talent Garden Calabiana, Milan. This is well-supported by the context provided in the document, which specifically mentions Mauro Rubin, the JoinPad CEO, taking the stage at the event. The answer is relevant to the question andconcise.\n\nFinal Evaluation: \nEvaluation:'

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("Netta1994/setfit_baai_gpt-4o_improved-cot-instructions_chat_few_shot_generated_remove_final_eva")
# Run inference
preds = model("Reasoning:
The provided answer incorrectly states who signed Kieron Freeman when he went on loan to Notts County instead of answering the question about Aaron Pryor's boxing manager. This answer does not relate to the provided question, making it contextually irrelevant and failing to address the inquiry about Aaron Pryor’s manager during his boxing career.

Final Evaluation:")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	33	77.1275	176

Label	Training Sample Count
0	200
1	208

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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0010	1	0.2056	-
0.0490	50	0.2533	-
0.0980	100	0.1804	-
0.1471	150	0.0762	-
0.1961	200	0.0731	-
0.2451	250	0.0501	-
0.2941	300	0.0336	-
0.3431	350	0.0197	-
0.3922	400	0.0105	-
0.4412	450	0.0049	-
0.4902	500	0.0031	-
0.5392	550	0.0024	-
0.5882	600	0.0021	-
0.6373	650	0.0018	-
0.6863	700	0.0017	-
0.7353	750	0.0018	-
0.7843	800	0.0016	-
0.8333	850	0.0015	-
0.8824	900	0.0017	-
0.9314	950	0.0015	-
0.9804	1000	0.0013	-

Framework Versions

Python: 3.10.14
SetFit: 1.1.0
Sentence Transformers: 3.1.1
Transformers: 4.44.0
PyTorch: 2.4.0+cu121
Datasets: 3.0.0
Tokenizers: 0.19.1

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}
}