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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: BAAI/bge-small-en-v1.5 |
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metrics: |
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- accuracy |
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widget: |
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- text: mostly works because of the universal themes , earnest performances ... and |
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excellent use of music by india 's popular gulzar and jagjit singh . |
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- text: in all the annals of the movies , few films have been this odd , inexplicable |
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and unpleasant . |
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- text: director charles stone iii applies more detail to the film 's music than to |
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the story line ; what 's best about drumline is its energy . |
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- text: there 's nothing exactly wrong here , but there 's not nearly enough that |
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's right . |
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- text: it 's a bad sign in a thriller when you instantly know whodunit . |
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pipeline_tag: text-classification |
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inference: true |
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model-index: |
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- name: SetFit with BAAI/bge-small-en-v1.5 |
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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: 0.8621636463481603 |
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name: Accuracy |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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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 [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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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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<!-- - **Language:** 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>'a sensitive , modest comic tragedy that works as both character study and symbolic examination of the huge economic changes sweeping modern china .'</li><li>'the year 2002 has conjured up more coming-of-age stories than seem possible , but take care of my cat emerges as the very best of them .'</li><li>'amy and matthew have a bit of a phony relationship , but the film works in spite of it .'</li></ul> | |
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| 0 | <ul><li>'works on the whodunit level as its larger themes get lost in the murk of its own making'</li><li>"one of those strained caper movies that 's hardly any fun to watch and begins to vaporize from your memory minutes after it ends ."</li><li>"shunji iwai 's all about lily chou chou is a beautifully shot , but ultimately flawed film about growing up in japan ."</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** | 0.8622 | |
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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("Jorgeutd/setfit-bge-small-v1.5-sst2-50-shot") |
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# Run inference |
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preds = model("it 's a bad sign in a thriller when you instantly know whodunit .") |
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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 | 3 | 21.31 | 50 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 50 | |
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| 1 | 50 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (10, 10) |
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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: 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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| 0.0031 | 1 | 0.2515 | - | |
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| 0.1567 | 50 | 0.2298 | - | |
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| 0.3135 | 100 | 0.2134 | - | |
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| 0.4702 | 150 | 0.0153 | - | |
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| 0.6270 | 200 | 0.0048 | - | |
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| 0.7837 | 250 | 0.0024 | - | |
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| 0.9404 | 300 | 0.0023 | - | |
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| 1.0972 | 350 | 0.0016 | - | |
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| 1.2539 | 400 | 0.0016 | - | |
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| 1.4107 | 450 | 0.001 | - | |
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| 1.5674 | 500 | 0.0013 | - | |
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| 1.7241 | 550 | 0.0008 | - | |
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| 1.8809 | 600 | 0.0008 | - | |
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| 2.0376 | 650 | 0.0007 | - | |
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| 2.1944 | 700 | 0.0008 | - | |
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| 2.3511 | 750 | 0.0008 | - | |
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| 2.5078 | 800 | 0.0007 | - | |
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| 2.6646 | 850 | 0.0006 | - | |
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| 2.8213 | 900 | 0.0006 | - | |
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| 2.9781 | 950 | 0.0005 | - | |
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| 3.1348 | 1000 | 0.0006 | - | |
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| 3.2915 | 1050 | 0.0006 | - | |
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| 3.4483 | 1100 | 0.0005 | - | |
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| 3.6050 | 1150 | 0.0005 | - | |
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| 3.7618 | 1200 | 0.0005 | - | |
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| 3.9185 | 1250 | 0.0005 | - | |
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| 4.0752 | 1300 | 0.0005 | - | |
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| 4.2320 | 1350 | 0.0004 | - | |
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| 4.3887 | 1400 | 0.0004 | - | |
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| 4.5455 | 1450 | 0.0004 | - | |
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| 4.7022 | 1500 | 0.0003 | - | |
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| 4.8589 | 1550 | 0.0006 | - | |
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| 5.0157 | 1600 | 0.0007 | - | |
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| 5.1724 | 1650 | 0.0004 | - | |
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| 5.3292 | 1700 | 0.0004 | - | |
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| 5.4859 | 1750 | 0.0004 | - | |
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| 5.6426 | 1800 | 0.0004 | - | |
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| 5.7994 | 1850 | 0.0003 | - | |
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| 5.9561 | 1900 | 0.0004 | - | |
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| 6.1129 | 1950 | 0.0003 | - | |
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| 6.2696 | 2000 | 0.0003 | - | |
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| 6.4263 | 2050 | 0.0005 | - | |
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| 6.5831 | 2100 | 0.0003 | - | |
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| 6.7398 | 2150 | 0.0003 | - | |
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| 6.8966 | 2200 | 0.0003 | - | |
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| 7.0533 | 2250 | 0.0003 | - | |
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| 7.2100 | 2300 | 0.0003 | - | |
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| 7.3668 | 2350 | 0.0003 | - | |
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| 7.5235 | 2400 | 0.0002 | - | |
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| 7.6803 | 2450 | 0.0003 | - | |
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| 7.8370 | 2500 | 0.0003 | - | |
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| 7.9937 | 2550 | 0.0003 | - | |
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| 8.1505 | 2600 | 0.0003 | - | |
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| 8.3072 | 2650 | 0.0003 | - | |
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| 8.4639 | 2700 | 0.0003 | - | |
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| 8.6207 | 2750 | 0.0003 | - | |
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| 8.7774 | 2800 | 0.0004 | - | |
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| 8.9342 | 2850 | 0.0002 | - | |
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| 9.0909 | 2900 | 0.0003 | - | |
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| 9.2476 | 2950 | 0.0004 | - | |
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| 9.4044 | 3000 | 0.0004 | - | |
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| 9.5611 | 3050 | 0.0003 | - | |
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| 9.7179 | 3100 | 0.0004 | - | |
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| 9.8746 | 3150 | 0.0003 | - | |
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### Framework Versions |
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- Python: 3.10.13 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.6.1 |
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- Transformers: 4.39.1 |
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- PyTorch: 2.1.0 |
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- Datasets: 2.18.0 |
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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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