metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
datasets:
  - hojzas/proj8-multilabel
metrics:
  - accuracy
widget:
  - text: >-
      def first_with_given_key(iterable, key=lambda x: x):\n    keys_used =
      {}\n    for item in iterable:\n        rp = repr(key(item))\n        if rp
      not in keys_used.keys():\n            keys_used[rp] =
      repr(item)\n            yield item
  - text: >-
      def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for
      i in iterable:\n        if key(i) not in keys:\n            yield
      i\n            keys.append(key(i))
  - text: >-
      def first_with_given_key(iterable, key=repr):\n    set_of_keys =
      set()\n    lambda_key = (lambda x: key(x))\n    for item in
      iterable:\n        key = lambda_key(item)\n        try:\n           
      key_for_set = hash(key)\n        except TypeError:\n           
      key_for_set = repr(key)\n        if key_for_set in
      set_of_keys:\n            continue\n       
      set_of_keys.add(key_for_set)\n        yield item
  - text: >-
      def first_with_given_key(iterable, key = lambda x: x):\n   
      found_keys={}\n    for i in iterable:\n        if key(i) not in
      found_keys.keys():\n            found_keys[key(i)]=i\n            yield i
  - text: >-
      def first_with_given_key(the_iterable, key=lambda x: x):\n   
      temp_keys=[]\n    for i in range(len(the_iterable)):\n        if
      (key(the_iterable[i]) not in temp_keys):\n           
      temp_keys.append(key(the_iterable[i]))\n            yield
      the_iterable[i]\n    del temp_keys
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
  emissions: 0.2716104726718793
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
  ram_total_size: 251.49160385131836
  hours_used: 0.005
base_model: sentence-transformers/paraphrase-mpnet-base-v2

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

This is a SetFit model trained on the hojzas/proj8-multilabel dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a OneVsRestClassifier instance
Maximum Sequence Length: 512 tokens
Training Dataset: hojzas/proj8-multilabel

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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("hojzas/setfit-proj8-multilabel")
# Run inference
preds = model("def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i))")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	43	92.5185	125

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
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0147	1	0.3001	-
0.7353	50	0.0104	-

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.000 kg of CO2
Hours Used: 0.005 hours

Training Hardware

On Cloud: No
GPU Model: No GPU used
CPU Model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
RAM Size: 251.49 GB

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.2.2
Transformers: 4.36.1
PyTorch: 2.1.2+cu121
Datasets: 2.14.7
Tokenizers: 0.15.1

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}
}