linkyeu/intent-routing-sweep-v3

SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 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 Type: SetFit
• Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 384 tokens
• Number of Classes: 6 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
1	'what can your do with your mcp tool db2 schema' 'is this normal?\nGC(47) Pause Young 234M->89M 12.5ms' 'what units is file_size in? what format is mimetype in'
0	'use mongo and postgres mcps to find test data' 'browser screenshot the error page' 'use the linear mcp to find my high priority issues'
2	'java\npublic void process() {\n if (user != null) {\n // stuff\n }\n}\n\nclean this up' "Duplicate class: 'ChunkCacheMetricsTest'" "python\ndef process(data):\n result = []\n for item in data:\n if item['active']:\n result.append(item['value'])\n return result\n\nuse list comprehension"
3	'review security scan then patch critical issues' 'analyze failing tests, group them, fix each group' '\n47 tests failing\n\ncategorize and fix each category'
5	'model architecture not supported, plan alternative approach' 'architecture diagram for wml' 'before we move on let me ask - can we develop a set of documents that break the implementation down into individual tasks - the first one being a simple core set of function that could deliver a a single starting point. and then add layers of function in subsequent tasks to methodically build out th'
4	'yes the linear mcp is finally connected' 'Usually we use a custom linter config here, not the standard one' 'ok it worked after i restarted the postgres mcp server'

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("tmp/best_model")
# Run inference
preds = model("lets fix the issues")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	12.3350	125

Label	Training Sample Count
0	43
1	80
2	88
3	56
4	61
5	84

Training Hyperparameters

• batch_size: (32, 32)
• num_epochs: (2, 2)
• max_steps: -1
• sampling_strategy: undersampling
• num_iterations: 12
• body_learning_rate: (5.430507564500918e-05, 5.430507564500918e-05)
• head_learning_rate: 0.0011800069021089953
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.006100049987809886
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0032	1	0.3642	-
0.0162	5	-	0.2610
0.0324	10	-	0.2849
0.0485	15	-	0.2427
0.0647	20	-	0.2171
0.0809	25	-	0.2144
0.0971	30	-	0.1991
0.1133	35	-	0.1893
0.1294	40	-	0.1862
0.1456	45	-	0.1785
0.1618	50	0.2183	0.1997
0.1780	55	-	0.1840
0.1942	60	-	0.1802
0.2104	65	-	0.1734
0.2265	70	-	0.1729
0.2427	75	-	0.1806
0.2589	80	-	0.1760
0.2751	85	-	0.1726
0.2913	90	-	0.1713
0.3074	95	-	0.1619
0.3236	100	0.0666	0.1645
0.3398	105	-	0.1649
0.3560	110	-	0.1538
0.3722	115	-	0.1639
0.3883	120	-	0.1761
0.4045	125	-	0.1702
0.4207	130	-	0.1781
0.4369	135	-	0.1778

Framework Versions

• Python: 3.12.12
• SetFit: 1.1.3
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.11.0+cu130
• Datasets: 4.8.4
• Tokenizers: 0.21.4

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}
}