You can deploy this model and start earning money today!

Netta1994/
setfit_baai_cybereason_gpt-4o_cot-instructions_remove_final_evaluation_e2_larger_trai

Name: setfit_baai_cybereason_gpt-4o_cot-instructions_remove_final_evaluation_e2_larger_trai
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

BAAI/bge-base-en-v1.5

finetune

model-index

Views

No views yet

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:\n1. Context Grounding: The answer is well-supported by the provided document. The document indicates that a dime is "one-tenth of a dollar" and has a monetary value of "10¢."\n2. Relevance: The answer directly addresses the specific question of the monetary value of a dime.\n3. Conciseness: The answer is clear, to the point, and does not include unnecessary information.\n\nFinal Result:'
'Reasoning:\n1. Context Grounding: The document lists "Set the investigation status" as a topic, indicating that it is possible to set the investigation status.\n2. Relevance: The answer "Yes" directly addresses the question of whether one can set the investigation status.\n3. Conciseness: The answer is brief and to the point.\n4. Specificity: The document explicitly mentions setting the investigation status, so the answer is specific to the asked question.\n5. Key/Value/Event Name: The relevant key/event is "Set the investigation status" which is correctlyidentified as being possible.\n\nFinal result:'
'Reasoning:\n\n1. Context Grounding: The answer is well-supported by the document, aligning perfectly with the specific benefits mentioned by the author in the document. It includes benefits like unapologetic "me" time, health, self-growth, patience, taking time to be still, accepting changing moods, responsibility for happiness, appreciation for the body, yoga's presence off the mat, and the importance of being open. These points are directly extracted from the provided content.\n \n2. Relevance: The answer focuses squarely on addressing the specific question asked — what benefits the author has experienced from their regular yoga practice. It avoids unrelated topics and stays on point.\n\n3. Conciseness: The answer is clear, concise, and directly lists the benefits without unnecessary elaboration. Each mentioned benefit corresponds to a specific point from the document, making it easyto verify and understand.\n\n**Final Result: **'

'Reasoning:\n1. Context Grounding: The answer is consistent with the suggestions found in the provided document. It mentions reducing salt intake, cutting processed foods and alcohol, and drinking more water, all of which are present in the document.\n2. Relevance: The answer stays focused on the main query about losing the last 10 pounds and provides actionable advice directly related to the document.\n3. Conciseness: The answer is a bit lengthy but stays mostly on point without much deviation.\n\nThe document provides a variety of methods for tackling the last 10 pounds, and the answer effectively consolidates some of these points. Although the response could be shorter and more concise, it appropriately addresses the question based on the document.\n\nFinal Result:'
'The given answer ..\\/..\\/_images\\/bal_https://elliott.biz/ does not match any of the Image URLs provided in the document. For step 5, the correct Image URL is ..\\/..\\/_images\\/bal_http://osborn-mendoza.info/.\n\nReasoning:\n1. Context Grounding: The answer is not supported by the provided document. The correct Image URL should be found in the list under the relevant step.\n2. Relevance: The answer must directly address the specific question by identifying the correct Image URL corresponding to step 5.\n3. Conciseness: The answer should be concise but accurate. The provided answer adds irrelevant information.\n4. Specifics: The provided document includes specific Image URLs for each step that must be matched correctly to the steps provided.\n5. Key, Value, and Event Name: The correct identification of the image URL for step 5 is critical here.\n\nFinal result:'
'Reasoning:\n1. Context Grounding: The provided answer is rooted in the document, which mentions that Amy Bloom finds starting a project hard and having to clear mental space, recalibrate, and become less involved in her everyday life.\n2. Relevance: The response accurately focuses on the challenges Bloom faces when starting a significant writing project, without deviating into irrelevant areas.\n3. Conciseness: The answer effectively summarizes the relevant information from the document, staying clear and to the point while avoiding unnecessary detail.\n\nFinal Result:'

Evaluation

Metrics

Label	Accuracy
all	0.7183

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_cybereason_gpt-4o_cot-instructions_remove_final_evaluation_e2_larger_trai")
# Run inference
preds = model("The percentage in the response status column indicates the total amount of successful completion of response actions.

Reasoning:
1. **Context Grounding**: The answer is well-supported by the document which states, \"percentage indicates the total amount of successful completion of response actions.\"
2. **Relevance**: The answer directly addresses the specific question asked about what the percentage in the response status column indicates.
3. **Conciseness**: The answer is succinct and to the point without unnecessary information.
4. **Specificity**: The answer is specific to what is being asked, detailing exactly what the percentage represents.
5. **Accuracy**: The answer provides the correct key/value as per the document.

Final result:")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	33	94.4664	198

Label	Training Sample Count
0	129
1	139

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (2, 2)
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
0.0015	1	0.1648	-
0.0746	50	0.2605	-
0.1493	100	0.2538	-
0.2239	150	0.2244	-
0.2985	200	0.1409	-
0.3731	250	0.0715	-
0.4478	300	0.0238	-
0.5224	350	0.0059	-
0.5970	400	0.0032	-
0.6716	450	0.0025	-
0.7463	500	0.0024	-
0.8209	550	0.0019	-
0.8955	600	0.0017	-
0.9701	650	0.0016	-
1.0448	700	0.0015	-
1.1194	750	0.0015	-
1.1940	800	0.0013	-
1.2687	850	0.0013	-
1.3433	900	0.0013	-
1.4179	950	0.0012	-
1.4925	1000	0.0013	-
1.5672	1050	0.0012	-
1.6418	1100	0.0011	-
1.7164	1150	0.0011	-
1.7910	1200	0.0011	-
1.8657	1250	0.0012	-
1.9403	1300	0.0011	-

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}
}