Hugging Face's logo

nlp-team-issai
/
setfit-me5-large-instruct-v3

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
setfit-me5-large-instruct-v3 / README.md
batyrme's picture
batyrme
Push model using huggingface_hub.
458ba08 verified
preview code
|
raw
history blame contribute delete
10.2 kB
metadata
base_model: intfloat/multilingual-e5-large-instruct
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      "Он подарил мне красивое кольцо и прекрасную вечеринку на нашу годовщину."
      Бұл мәтінді қазақ тіліне аударып беріңізші.
  - text: Would you please put that cigarette out? I get sick on it.
  - text: Сәлем!
  - text: Никусор Эшану
  - text: >-
      How time flies! We have been lovers for nearly a year. We hit it off
      instantly.
inference: true
model-index:
  - name: SetFit with intfloat/multilingual-e5-large-instruct
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9955398215928637
            name: Accuracy

SetFit with intfloat/multilingual-e5-large-instruct

This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-large-instruct 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 Sources

Model Labels

Label Examples
rag
  • 'Саксон эпизоды туралы қандай тарихи құжатта мәлімет берілген?'
  • 'Uttermost өзінің жарыс мансабында қандай маңызды жетістіктерге қол жеткізді?'
  • 'Ричард Бахтелл'
no_rag
  • 'Just a moment, please.'
  • 'орыс тіліндегі "Я рабочий." сөйлемінің қазақ тіліндегі аудармасы не?'
  • 'You look tired. Did you sleep well last night?'

Evaluation

Metrics

Label Accuracy
all 0.9955

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("nlp-team-issai/setfit-me5-large-instruct-v3")
# Run inference
preds = model("Сәлем!")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 10.0022 138
Label Training Sample Count
no_rag 218
rag 241

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • 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
0.0003 1 0.3567 -
0.0151 50 0.2851 -
0.0302 100 0.0943 -
0.0452 150 0.0123 -
0.0603 200 0.0099 -
0.0754 250 0.0056 -
0.0905 300 0.0011 -
0.1056 350 0.0003 -
0.1207 400 0.0002 -
0.1357 450 0.0001 -
0.1508 500 0.0001 -
0.1659 550 0.0001 -
0.1810 600 0.0001 -
0.1961 650 0.0001 -
0.2112 700 0.0001 -
0.2262 750 0.0001 -
0.2413 800 0.0001 -
0.2564 850 0.0001 -
0.2715 900 0.0001 -
0.2866 950 0.0001 -
0.3017 1000 0.0001 -
0.3167 1050 0.0001 -
0.3318 1100 0.0001 -
0.3469 1150 0.0001 -
0.3620 1200 0.0001 -
0.3771 1250 0.0001 -
0.3922 1300 0.0001 -
0.4072 1350 0.0001 -
0.4223 1400 0.0 -
0.4374 1450 0.0 -
0.4525 1500 0.0 -
0.4676 1550 0.0 -
0.4827 1600 0.0 -
0.4977 1650 0.0 -
0.5128 1700 0.0 -
0.5279 1750 0.0 -
0.5430 1800 0.0 -
0.5581 1850 0.0 -
0.5732 1900 0.0 -
0.5882 1950 0.0 -
0.6033 2000 0.0 -
0.6184 2050 0.0 -
0.6335 2100 0.0 -
0.6486 2150 0.0 -
0.6637 2200 0.0 -
0.6787 2250 0.0 -
0.6938 2300 0.0 -
0.7089 2350 0.0 -
0.7240 2400 0.0 -
0.7391 2450 0.0 -
0.7541 2500 0.0 -
0.7692 2550 0.0 -
0.7843 2600 0.0 -
0.7994 2650 0.0 -
0.8145 2700 0.0 -
0.8296 2750 0.0 -
0.8446 2800 0.0 -
0.8597 2850 0.0 -
0.8748 2900 0.0 -
0.8899 2950 0.0 -
0.9050 3000 0.0 -
0.9201 3050 0.0 -
0.9351 3100 0.0 -
0.9502 3150 0.0 -
0.9653 3200 0.0 -
0.9804 3250 0.0 -
0.9955 3300 0.0 -

Framework Versions

  • Python: 3.12.5
  • SetFit: 1.1.0
  • Sentence Transformers: 3.2.0
  • Transformers: 4.45.2
  • PyTorch: 2.4.0+cu121
  • Datasets: 3.0.1
  • Tokenizers: 0.20.0

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}
}