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--- |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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: Is it available? |
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- text: Est-il possible de fixer une visite? |
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- text: Where is it located? |
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- text: Pouvez-vous me parler des projets disponibles? |
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- text: What’s the process to reserve? |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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 [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) |
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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:** 128 tokens |
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- **Number of Classes:** 9 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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| schedule_a_visit | <ul><li>'I’d like to schedule a visit'</li><li>'Je voudrais planifier une visite'</li><li>'Puis-je programmer une visite?'</li></ul> | |
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| check_availability | <ul><li>'Est-ce encore disponible?'</li><li>'Is this still available?'</li><li>'Can I check availability?'</li></ul> | |
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| amenities_and_features | <ul><li>'Parlez-moi des fonctionnalités du bien'</li><li>'Tell me the features of the property'</li><li>'Quels sont les équipements disponibles?'</li></ul> | |
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| payment_plan | <ul><li>'Pouvez-vous me parler du plan de paiement?'</li><li>'Quels sont les modes de paiement disponibles?'</li><li>'What are the payment options?'</li></ul> | |
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| reservation_process | <ul><li>'Tell me about the reservation process'</li><li>'Pouvez-vous m’expliquer le processus de réservation?'</li><li>'Comment puis-je faire une réservation?'</li></ul> | |
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| location_details | <ul><li>'Où est-ce situé?'</li><li>'Can you tell me the location details?'</li><li>'What’s the address?'</li></ul> | |
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| pricing_details | <ul><li>'How much does it cost?'</li><li>'Tell me the pricing details'</li><li>'Combien ça coûte?'</li></ul> | |
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| option_process | <ul><li>'Tell me about the option process'</li><li>'Parlez-moi du processus des options'</li><li>'Quels sont mes choix?'</li></ul> | |
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| information_on_projects | <ul><li>'Can you give me information about the projects?'</li><li>'I need details on the available projects'</li><li>'Quels sont les projets disponibles ?'</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** | 1.0 | |
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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("ali170506/chab") |
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# Run inference |
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preds = model("Is it available?") |
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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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*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 | 3 | 5.2222 | 8 | |
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| Label | Training Sample Count | |
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|:------------------------|:----------------------| |
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| information_on_projects | 3 | |
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| pricing_details | 3 | |
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| location_details | 3 | |
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| amenities_and_features | 3 | |
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| check_availability | 3 | |
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| schedule_a_visit | 3 | |
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| reservation_process | 3 | |
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| option_process | 3 | |
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| payment_plan | 3 | |
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### Training Hyperparameters |
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- batch_size: (4, 4) |
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- num_epochs: (4, 4) |
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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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- seed: 42 |
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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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| 0.0062 | 1 | 0.0311 | - | |
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| 0.0617 | 10 | 0.0989 | - | |
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| 0.1235 | 20 | 0.0036 | - | |
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| 0.1852 | 30 | 0.0121 | - | |
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| 0.2469 | 40 | 0.0209 | - | |
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| 0.3086 | 50 | 0.001 | - | |
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| 0.3704 | 60 | 0.0067 | - | |
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| 0.4321 | 70 | 0.017 | - | |
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| 0.4938 | 80 | 0.0037 | - | |
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| 0.5556 | 90 | 0.012 | - | |
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| 0.6173 | 100 | 0.0009 | - | |
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| 0.6790 | 110 | 0.0044 | - | |
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| 0.7407 | 120 | 0.0014 | - | |
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| 0.8025 | 130 | 0.0006 | - | |
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| 0.8642 | 140 | 0.0016 | - | |
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| 0.9259 | 150 | 0.0024 | - | |
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| 0.9877 | 160 | 0.0011 | - | |
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| 1.0 | 162 | - | 0.0164 | |
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| 1.0494 | 170 | 0.0019 | - | |
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| 1.1111 | 180 | 0.0017 | - | |
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| 1.1728 | 190 | 0.0004 | - | |
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| 1.2346 | 200 | 0.0008 | - | |
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| 1.2963 | 210 | 0.0012 | - | |
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| 1.3580 | 220 | 0.0009 | - | |
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| 1.4198 | 230 | 0.0006 | - | |
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| 1.4815 | 240 | 0.001 | - | |
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| 1.5432 | 250 | 0.0009 | - | |
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| 1.6049 | 260 | 0.0015 | - | |
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| 1.6667 | 270 | 0.0016 | - | |
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| 1.7284 | 280 | 0.0009 | - | |
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| 1.7901 | 290 | 0.0005 | - | |
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| 1.8519 | 300 | 0.0009 | - | |
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| 1.9136 | 310 | 0.0009 | - | |
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| 1.9753 | 320 | 0.0008 | - | |
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| 2.0 | 324 | - | 0.0138 | |
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| 2.0370 | 330 | 0.0011 | - | |
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| 2.0988 | 340 | 0.0016 | - | |
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| 2.1605 | 350 | 0.0006 | - | |
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| 2.2222 | 360 | 0.0012 | - | |
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| 2.2840 | 370 | 0.0014 | - | |
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| 2.3457 | 380 | 0.0009 | - | |
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| 2.4074 | 390 | 0.0008 | - | |
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| 2.4691 | 400 | 0.0003 | - | |
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| 2.5309 | 410 | 0.0002 | - | |
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| 2.5926 | 420 | 0.0007 | - | |
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| 2.6543 | 430 | 0.001 | - | |
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| 2.7160 | 440 | 0.0008 | - | |
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| 2.7778 | 450 | 0.0008 | - | |
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| 2.8395 | 460 | 0.0003 | - | |
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| 2.9012 | 470 | 0.0004 | - | |
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| 2.9630 | 480 | 0.0003 | - | |
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| **3.0** | **486** | **-** | **0.0129** | |
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| 3.0247 | 490 | 0.0013 | - | |
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| 3.0864 | 500 | 0.0006 | - | |
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| 3.1481 | 510 | 0.0008 | - | |
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| 3.2099 | 520 | 0.0001 | - | |
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| 3.2716 | 530 | 0.0007 | - | |
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| 3.3333 | 540 | 0.0004 | - | |
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| 3.3951 | 550 | 0.0004 | - | |
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| 3.4568 | 560 | 0.0003 | - | |
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| 3.5185 | 570 | 0.0003 | - | |
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| 3.5802 | 580 | 0.0002 | - | |
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| 3.6420 | 590 | 0.0002 | - | |
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| 3.7037 | 600 | 0.0002 | - | |
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| 3.7654 | 610 | 0.0007 | - | |
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| 3.8272 | 620 | 0.0007 | - | |
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| 3.8889 | 630 | 0.0007 | - | |
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| 3.9506 | 640 | 0.0003 | - | |
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| 4.0 | 648 | - | 0.0129 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.37.0 |
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- PyTorch: 2.4.1+cu121 |
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- Datasets: 3.0.1 |
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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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