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--- |
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base_model: intfloat/multilingual-e5-large-instruct |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: '"Он подарил мне красивое кольцо и прекрасную вечеринку на нашу годовщину." |
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Бұл мәтінді қазақ тіліне аударып беріңізші.' |
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- text: Would you please put that cigarette out? I get sick on it. |
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- text: Сәлем! |
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- text: Никусор Эшану |
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- text: How time flies! We have been lovers for nearly a year. We hit it off instantly. |
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inference: true |
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model-index: |
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- name: SetFit with intfloat/multilingual-e5-large-instruct |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.9955398215928637 |
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name: Accuracy |
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--- |
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# SetFit with intfloat/multilingual-e5-large-instruct |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:-------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| rag | <ul><li>'Саксон эпизоды туралы қандай тарихи құжатта мәлімет берілген?'</li><li>'Uttermost өзінің жарыс мансабында қандай маңызды жетістіктерге қол жеткізді?'</li><li>'Ричард Бахтелл'</li></ul> | |
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| no_rag | <ul><li>'Just a moment, please.'</li><li>'орыс тіліндегі "Я рабочий." сөйлемінің қазақ тіліндегі аудармасы не?'</li><li>'You look tired. Did you sleep well last night?'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.9955 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("nlp-team-issai/setfit-me5-large-instruct-v3") |
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# Run inference |
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preds = model("Сәлем!") |
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``` |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 10.0022 | 138 | |
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| Label | Training Sample Count | |
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|:-------|:----------------------| |
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| no_rag | 218 | |
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| rag | 241 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0003 | 1 | 0.3567 | - | |
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| 0.0151 | 50 | 0.2851 | - | |
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| 0.0302 | 100 | 0.0943 | - | |
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| 0.0452 | 150 | 0.0123 | - | |
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| 0.0603 | 200 | 0.0099 | - | |
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| 0.0754 | 250 | 0.0056 | - | |
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| 0.0905 | 300 | 0.0011 | - | |
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| 0.1056 | 350 | 0.0003 | - | |
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| 0.1207 | 400 | 0.0002 | - | |
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| 0.1357 | 450 | 0.0001 | - | |
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| 0.1508 | 500 | 0.0001 | - | |
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| 0.1659 | 550 | 0.0001 | - | |
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| 0.1810 | 600 | 0.0001 | - | |
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| 0.1961 | 650 | 0.0001 | - | |
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| 0.2112 | 700 | 0.0001 | - | |
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| 0.2262 | 750 | 0.0001 | - | |
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| 0.2413 | 800 | 0.0001 | - | |
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| 0.2564 | 850 | 0.0001 | - | |
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| 0.2715 | 900 | 0.0001 | - | |
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| 0.2866 | 950 | 0.0001 | - | |
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| 0.3017 | 1000 | 0.0001 | - | |
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| 0.3167 | 1050 | 0.0001 | - | |
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| 0.3318 | 1100 | 0.0001 | - | |
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| 0.3469 | 1150 | 0.0001 | - | |
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| 0.3620 | 1200 | 0.0001 | - | |
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| 0.3771 | 1250 | 0.0001 | - | |
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| 0.3922 | 1300 | 0.0001 | - | |
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| 0.4072 | 1350 | 0.0001 | - | |
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| 0.4223 | 1400 | 0.0 | - | |
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| 0.4374 | 1450 | 0.0 | - | |
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| 0.4525 | 1500 | 0.0 | - | |
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| 0.4676 | 1550 | 0.0 | - | |
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| 0.4827 | 1600 | 0.0 | - | |
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| 0.4977 | 1650 | 0.0 | - | |
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| 0.5128 | 1700 | 0.0 | - | |
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| 0.5279 | 1750 | 0.0 | - | |
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| 0.5430 | 1800 | 0.0 | - | |
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| 0.5581 | 1850 | 0.0 | - | |
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| 0.5732 | 1900 | 0.0 | - | |
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| 0.5882 | 1950 | 0.0 | - | |
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| 0.6033 | 2000 | 0.0 | - | |
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| 0.6184 | 2050 | 0.0 | - | |
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| 0.6335 | 2100 | 0.0 | - | |
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| 0.6486 | 2150 | 0.0 | - | |
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| 0.6637 | 2200 | 0.0 | - | |
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| 0.6787 | 2250 | 0.0 | - | |
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| 0.6938 | 2300 | 0.0 | - | |
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| 0.7089 | 2350 | 0.0 | - | |
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| 0.7240 | 2400 | 0.0 | - | |
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| 0.7391 | 2450 | 0.0 | - | |
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| 0.7541 | 2500 | 0.0 | - | |
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| 0.7692 | 2550 | 0.0 | - | |
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| 0.7843 | 2600 | 0.0 | - | |
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| 0.7994 | 2650 | 0.0 | - | |
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| 0.8145 | 2700 | 0.0 | - | |
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| 0.8296 | 2750 | 0.0 | - | |
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| 0.8446 | 2800 | 0.0 | - | |
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| 0.8597 | 2850 | 0.0 | - | |
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| 0.8748 | 2900 | 0.0 | - | |
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| 0.8899 | 2950 | 0.0 | - | |
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| 0.9050 | 3000 | 0.0 | - | |
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| 0.9201 | 3050 | 0.0 | - | |
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| 0.9351 | 3100 | 0.0 | - | |
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| 0.9502 | 3150 | 0.0 | - | |
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| 0.9653 | 3200 | 0.0 | - | |
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| 0.9804 | 3250 | 0.0 | - | |
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| 0.9955 | 3300 | 0.0 | - | |
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### Framework Versions |
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- Python: 3.12.5 |
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- SetFit: 1.1.0 |
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- Sentence Transformers: 3.2.0 |
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- Transformers: 4.45.2 |
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- PyTorch: 2.4.0+cu121 |
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- Datasets: 3.0.1 |
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- Tokenizers: 0.20.0 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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