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--- |
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library_name: setfit |
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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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base_model: intfloat/multilingual-e5-small |
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metrics: |
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- accuracy |
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widget: |
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- text: 'query: Ναι, ας πάμε!' |
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- text: 'query: 信じられない!それはすごいね。熊を見たなんて!' |
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- text: 'query: Taky dobře. Potkáme se zítra?' |
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- text: 'query: Sì, mi farebbe piacere. A tra poco!' |
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- text: 'query: Γεια σου, πώς είσαι;' |
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pipeline_tag: text-classification |
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inference: true |
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--- |
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# SetFit with intfloat/multilingual-e5-small |
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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-small](https://huggingface.co/intfloat/multilingual-e5-small) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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-small](https://huggingface.co/intfloat/multilingual-e5-small) |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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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| 1 | <ul><li>'query: ਚੰਗਾ ਜੀ, ਫਿਰ ਮਿਲਦੇ ਹਾਂ.'</li><li>'query: Agur, gero arte.'</li><li>"query: Me'n vaig ara."</li></ul> | |
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| 0 | <ul><li>'query: Dobro, hvala. Kaj pa ti?'</li><li>'query: हाँ अगली बार जब तुम जाओ मुझे भी ले चलो मुझे भी प्रकृति में और गतिविधियाँ करनी हैं'</li><li>'query: Mirë, faleminderit. Po ju?'</li></ul> | |
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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("setfit_model_id") |
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# Run inference |
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preds = model("query: Ναι, ας πάμε!") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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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 | 2 | 7.4364 | 21 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 292 | |
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| 1 | 290 | |
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### Training Hyperparameters |
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- batch_size: (16, 2) |
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- num_epochs: (1, 16) |
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- max_steps: -1 |
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- sampling_strategy: undersampling |
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- body_learning_rate: (1e-05, 1e-05) |
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- head_learning_rate: 0.001 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.1 |
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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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- seed: 42 |
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- run_name: intfloat/multilingual-e5-small |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:----------:|:--------:|:-------------:|:---------------:| |
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| 0.0001 | 1 | 0.3645 | - | |
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| 0.0047 | 50 | 0.3527 | - | |
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| 0.0094 | 100 | 0.3424 | 0.3165 | |
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| 0.0142 | 150 | 0.3108 | - | |
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| 0.0189 | 200 | 0.2684 | 0.2215 | |
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| 0.0236 | 250 | 0.2197 | - | |
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| 0.0283 | 300 | 0.1707 | 0.1792 | |
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| 0.0331 | 350 | 0.1501 | - | |
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| 0.0378 | 400 | 0.0865 | 0.1607 | |
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| 0.0425 | 450 | 0.0534 | - | |
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| 0.0472 | 500 | 0.0307 | 0.1519 | |
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| 0.0520 | 550 | 0.0342 | - | |
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| 0.0567 | 600 | 0.0078 | 0.1478 | |
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| 0.0614 | 650 | 0.0144 | - | |
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| 0.0661 | 700 | 0.0658 | 0.1399 | |
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| 0.0709 | 750 | 0.0021 | - | |
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| 0.0756 | 800 | 0.0009 | 0.1512 | |
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| 0.0803 | 850 | 0.0005 | - | |
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| 0.0850 | 900 | 0.0018 | 0.1516 | |
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| 0.0897 | 950 | 0.0011 | - | |
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| 0.0945 | 1000 | 0.0012 | 0.1541 | |
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| 0.0992 | 1050 | 0.0003 | - | |
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| 0.1039 | 1100 | 0.0003 | 0.1415 | |
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| 0.1086 | 1150 | 0.0003 | - | |
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| 0.1134 | 1200 | 0.0002 | 0.1442 | |
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| 0.1181 | 1250 | 0.0006 | - | |
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| 0.1228 | 1300 | 0.0002 | 0.1298 | |
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| 0.1275 | 1350 | 0.0002 | - | |
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| 0.1323 | 1400 | 0.0001 | 0.1356 | |
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| 0.1370 | 1450 | 0.0002 | - | |
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| 0.1417 | 1500 | 0.0003 | 0.1493 | |
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| 0.1464 | 1550 | 0.0003 | - | |
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| 0.1512 | 1600 | 0.0002 | 0.15 | |
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| 0.1559 | 1650 | 0.0002 | - | |
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| 0.1606 | 1700 | 0.0003 | 0.1469 | |
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| 0.1653 | 1750 | 0.0001 | - | |
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| 0.1701 | 1800 | 0.0001 | 0.1554 | |
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| 0.1748 | 1850 | 0.0002 | - | |
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| 0.1795 | 1900 | 0.0001 | 0.168 | |
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| 0.1842 | 1950 | 0.0001 | - | |
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| 0.1889 | 2000 | 0.0004 | 0.1568 | |
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| 0.1937 | 2050 | 0.0001 | - | |
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| 0.1984 | 2100 | 0.0001 | 0.1513 | |
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| 0.2031 | 2150 | 0.0001 | - | |
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| 0.2078 | 2200 | 0.0003 | 0.1503 | |
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| 0.2126 | 2250 | 0.0002 | - | |
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| 0.2173 | 2300 | 0.0604 | 0.155 | |
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| 0.2220 | 2350 | 0.0001 | - | |
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| 0.2267 | 2400 | 0.0002 | 0.1739 | |
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| 0.2315 | 2450 | 0.0006 | - | |
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| 0.2362 | 2500 | 0.0002 | 0.1558 | |
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| 0.2409 | 2550 | 0.0002 | - | |
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| 0.2456 | 2600 | 0.0001 | 0.1393 | |
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| 0.2504 | 2650 | 0.0004 | - | |
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| 0.2551 | 2700 | 0.0003 | 0.1642 | |
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| 0.2598 | 2750 | 0.0002 | - | |
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| 0.2645 | 2800 | 0.0002 | 0.1776 | |
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| 0.2692 | 2850 | 0.0 | - | |
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| 0.2740 | 2900 | 0.0002 | 0.1794 | |
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| 0.2787 | 2950 | 0.0001 | - | |
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| 0.2834 | 3000 | 0.0001 | 0.183 | |
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| 0.2881 | 3050 | 0.0001 | - | |
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| 0.2929 | 3100 | 0.0001 | 0.1805 | |
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| 0.2976 | 3150 | 0.0001 | - | |
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| 0.3023 | 3200 | 0.0001 | 0.1757 | |
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| 0.3070 | 3250 | 0.0001 | - | |
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| 0.3118 | 3300 | 0.0001 | 0.1302 | |
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| 0.3165 | 3350 | 0.0001 | - | |
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| 0.3212 | 3400 | 0.0001 | 0.1348 | |
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| 0.3259 | 3450 | 0.0001 | - | |
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| 0.3307 | 3500 | 0.0005 | 0.1623 | |
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| 0.3354 | 3550 | 0.0 | - | |
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| 0.3401 | 3600 | 0.0 | 0.1286 | |
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| 0.3448 | 3650 | 0.0 | - | |
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| 0.3496 | 3700 | 0.0001 | 0.1736 | |
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| 0.3543 | 3750 | 0.0 | - | |
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| 0.3590 | 3800 | 0.0 | 0.127 | |
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| 0.3637 | 3850 | 0.0 | - | |
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| 0.3684 | 3900 | 0.0001 | 0.1231 | |
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| 0.3732 | 3950 | 0.0 | - | |
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| **0.3779** | **4000** | **0.0001** | **0.1261** | |
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| 0.3826 | 4050 | 0.0001 | - | |
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| 0.3873 | 4100 | 0.0 | 0.1216 | |
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| 0.3921 | 4150 | 0.0 | - | |
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| 0.3968 | 4200 | 0.0 | 0.1404 | |
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| 0.4015 | 4250 | 0.0 | - | |
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| 0.4062 | 4300 | 0.0 | 0.1466 | |
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| 0.4110 | 4350 | 0.0 | - | |
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| 0.4157 | 4400 | 0.0 | 0.1482 | |
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| 0.4204 | 4450 | 0.0 | - | |
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| 0.4251 | 4500 | 0.0 | 0.1547 | |
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| 0.4299 | 4550 | 0.0 | - | |
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| 0.4346 | 4600 | 0.0 | 0.1566 | |
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| 0.4393 | 4650 | 0.0 | - | |
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| 0.4440 | 4700 | 0.0 | 0.1684 | |
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| 0.4487 | 4750 | 0.0 | - | |
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| 0.4535 | 4800 | 0.0 | 0.1746 | |
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| 0.4582 | 4850 | 0.0 | - | |
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| 0.4629 | 4900 | 0.0 | 0.167 | |
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| 0.4676 | 4950 | 0.0 | - | |
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| 0.4724 | 5000 | 0.0001 | 0.1683 | |
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| 0.4771 | 5050 | 0.0 | - | |
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| 0.4818 | 5100 | 0.0 | 0.1693 | |
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| 0.4865 | 5150 | 0.0 | - | |
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| 0.4913 | 5200 | 0.0 | 0.1694 | |
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| 0.4960 | 5250 | 0.0 | - | |
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| 0.5007 | 5300 | 0.0 | 0.162 | |
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| 0.5054 | 5350 | 0.0 | - | |
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| 0.5102 | 5400 | 0.0 | 0.1388 | |
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| 0.5149 | 5450 | 0.0 | - | |
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| 0.5196 | 5500 | 0.0 | 0.1353 | |
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| 0.5243 | 5550 | 0.0 | - | |
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| 0.5291 | 5600 | 0.0 | 0.1401 | |
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| 0.5338 | 5650 | 0.0 | - | |
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| 0.5385 | 5700 | 0.0 | 0.1466 | |
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| 0.5432 | 5750 | 0.0 | - | |
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| 0.5479 | 5800 | 0.0 | 0.1529 | |
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| 0.5527 | 5850 | 0.0 | - | |
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| 0.5574 | 5900 | 0.0 | 0.1488 | |
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| 0.5621 | 5950 | 0.0 | - | |
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| 0.5668 | 6000 | 0.0 | 0.147 | |
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| 0.5716 | 6050 | 0.0 | - | |
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| 0.5763 | 6100 | 0.0 | 0.1493 | |
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| 0.5810 | 6150 | 0.0 | - | |
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| 0.5857 | 6200 | 0.0 | 0.1525 | |
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| 0.5905 | 6250 | 0.0 | - | |
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| 0.5952 | 6300 | 0.0 | 0.1505 | |
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| 0.5999 | 6350 | 0.0 | - | |
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| 0.6046 | 6400 | 0.0 | 0.1554 | |
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| 0.6094 | 6450 | 0.0 | - | |
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| 0.6141 | 6500 | 0.0 | 0.1546 | |
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| 0.6188 | 6550 | 0.0 | - | |
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| 0.6235 | 6600 | 0.0 | 0.1598 | |
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| 0.6282 | 6650 | 0.0 | - | |
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| 0.6330 | 6700 | 0.0 | 0.179 | |
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| 0.6377 | 6750 | 0.0 | - | |
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| 0.6424 | 6800 | 0.0 | 0.1719 | |
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| 0.6471 | 6850 | 0.0001 | - | |
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| 0.6519 | 6900 | 0.0 | 0.1812 | |
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| 0.6566 | 6950 | 0.0 | - | |
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| 0.6613 | 7000 | 0.0 | 0.1648 | |
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| 0.6660 | 7050 | 0.0 | - | |
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| 0.6708 | 7100 | 0.0 | 0.1717 | |
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| 0.6755 | 7150 | 0.0 | - | |
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| 0.6802 | 7200 | 0.0 | 0.1793 | |
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| 0.6849 | 7250 | 0.0 | - | |
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| 0.6897 | 7300 | 0.0 | 0.1766 | |
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| 0.6944 | 7350 | 0.0 | - | |
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| 0.6991 | 7400 | 0.0 | 0.177 | |
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| 0.7038 | 7450 | 0.0 | - | |
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| 0.7085 | 7500 | 0.0 | 0.1749 | |
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| 0.7133 | 7550 | 0.0 | - | |
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| 0.7180 | 7600 | 0.0 | 0.1814 | |
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| 0.7227 | 7650 | 0.0 | - | |
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| 0.7274 | 7700 | 0.0 | 0.1742 | |
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| 0.7322 | 7750 | 0.0 | - | |
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| 0.7369 | 7800 | 0.0 | 0.179 | |
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| 0.7416 | 7850 | 0.0 | - | |
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| 0.7463 | 7900 | 0.0 | 0.1767 | |
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| 0.7511 | 7950 | 0.0 | - | |
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| 0.7558 | 8000 | 0.0 | 0.1809 | |
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| 0.7605 | 8050 | 0.0 | - | |
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| 0.7652 | 8100 | 0.0 | 0.1767 | |
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| 0.7700 | 8150 | 0.0 | - | |
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| 0.7747 | 8200 | 0.0 | 0.1698 | |
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| 0.7794 | 8250 | 0.0 | - | |
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| 0.7841 | 8300 | 0.0 | 0.1772 | |
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| 0.7889 | 8350 | 0.0 | - | |
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| 0.7936 | 8400 | 0.0 | 0.1722 | |
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| 0.7983 | 8450 | 0.0 | - | |
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| 0.8030 | 8500 | 0.0 | 0.1671 | |
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| 0.8077 | 8550 | 0.0 | - | |
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| 0.8125 | 8600 | 0.0 | 0.181 | |
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| 0.8172 | 8650 | 0.0 | - | |
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| 0.8219 | 8700 | 0.0 | 0.1788 | |
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| 0.8266 | 8750 | 0.0 | - | |
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| 0.8314 | 8800 | 0.0 | 0.1784 | |
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| 0.8361 | 8850 | 0.0 | - | |
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| 0.8408 | 8900 | 0.0 | 0.1806 | |
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| 0.8455 | 8950 | 0.0 | - | |
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| 0.8503 | 9000 | 0.0 | 0.1783 | |
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| 0.8550 | 9050 | 0.0 | - | |
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| 0.8597 | 9100 | 0.0 | 0.1783 | |
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| 0.8644 | 9150 | 0.0 | - | |
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| 0.8692 | 9200 | 0.0 | 0.1785 | |
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| 0.8739 | 9250 | 0.0 | - | |
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| 0.8786 | 9300 | 0.0 | 0.1772 | |
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| 0.8833 | 9350 | 0.0 | - | |
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| 0.8880 | 9400 | 0.0 | 0.1816 | |
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| 0.8928 | 9450 | 0.0 | - | |
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| 0.8975 | 9500 | 0.0 | 0.1794 | |
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| 0.9022 | 9550 | 0.0 | - | |
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| 0.9069 | 9600 | 0.0 | 0.168 | |
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| 0.9117 | 9650 | 0.0 | - | |
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| 0.9164 | 9700 | 0.0 | 0.1771 | |
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| 0.9211 | 9750 | 0.0 | - | |
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| 0.9258 | 9800 | 0.0 | 0.1675 | |
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| 0.9306 | 9850 | 0.0 | - | |
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| 0.9353 | 9900 | 0.0 | 0.1746 | |
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| 0.9400 | 9950 | 0.0 | - | |
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| 0.9447 | 10000 | 0.0 | 0.1769 | |
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| 0.9495 | 10050 | 0.0 | - | |
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| 0.9542 | 10100 | 0.0 | 0.177 | |
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| 0.9589 | 10150 | 0.0 | - | |
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| 0.9636 | 10200 | 0.0 | 0.1771 | |
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| 0.9684 | 10250 | 0.0 | - | |
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| 0.9731 | 10300 | 0.0 | 0.1794 | |
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| 0.9778 | 10350 | 0.0 | - | |
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| 0.9825 | 10400 | 0.0 | 0.177 | |
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| 0.9872 | 10450 | 0.0 | - | |
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| 0.9920 | 10500 | 0.0 | 0.1794 | |
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| 0.9967 | 10550 | 0.0 | - | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.11 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.7.0 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.15.2 |
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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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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