linkyeu/intent-routing-sweep-v4

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: (8, 8)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: unique
• num_iterations: 24
• body_learning_rate: (5.379125214937585e-05, 5.379125214937585e-05)
• head_learning_rate: 0.009661295989662243
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.12909445918143356
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0004	1	0.5108	-
0.0040	10	-	0.3669
0.0081	20	-	0.3571
0.0121	30	-	0.3346
0.0162	40	-	0.2993
0.0202	50	0.3376	0.2637
0.0243	60	-	0.2393
0.0283	70	-	0.2337
0.0324	80	-	0.2334
0.0364	90	-	0.2300
0.0405	100	0.2475	0.2319
0.0445	110	-	0.2254
0.0485	120	-	0.2203
0.0526	130	-	0.2157
0.0566	140	-	0.2324
0.0607	150	0.2205	0.2153
0.0647	160	-	0.2061
0.0688	170	-	0.1946
0.0728	180	-	0.2024
0.0769	190	-	0.1932
0.0809	200	0.1882	0.2088
0.0850	210	-	0.1758
0.0890	220	-	0.1694
0.0930	230	-	0.1885
0.0971	240	-	0.2039
0.1011	250	0.1434	0.1648
0.1052	260	-	0.1499
0.1092	270	-	0.1722
0.1133	280	-	0.1763
0.1173	290	-	0.1768
0.1214	300	0.0993	0.1632
0.1254	310	-	0.1650

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}
}