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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: mental/mental-bert-base-uncased |
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metrics: |
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- accuracy |
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widget: |
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- text: I am going through a divorce. He is extremely angry. He refuses to physically |
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assist me with our teenager daughter. I have no extended family support. Often |
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times, I feel overwhelmed, tired, and joyless. I feel out of control, sad and |
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depressed on a daily basis. I am just going through the motions of life every |
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day. I am in my mid-50s. I have almost 29 years on my job. How can I handle this? |
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- text: Every winter I find myself getting sad because of the weather. How can I fight |
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this? |
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- text: Adjusting to life after significant life changes |
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- text: "I have so many issues to address. I have a history of sexual abuse, I’m a\ |
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\ breast cancer survivor and I am a lifetime insomniac. I have a long history\ |
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\ of depression and I’m beginning to have anxiety. I have low self esteem but\ |
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\ I’ve been happily married for almost 35 years.\n I’ve never had counseling\ |
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\ about any of this. Do I have too many issues to address in counseling?" |
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- text: Planning a DIY home renovation project. |
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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 mental/mental-bert-base-uncased |
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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: 0.9882352941176471 |
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name: Accuracy |
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--- |
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# SetFit with mental/mental-bert-base-uncased |
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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 [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) 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:** [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) |
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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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<!-- - **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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| True | <ul><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'Experiencing extreme mood swings not related to external circumstances.'</li></ul> | |
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| False | <ul><li>'Guide to learning a new language'</li><li>'Learning about the historical significance of the Silk Road.'</li><li>'Exploring historical landmarks in Europe'</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** | 0.9882 | |
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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("richie-ghost/setfit-mental-bert-base-uncased-MH-Topic-Check") |
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# Run inference |
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preds = model("Planning a DIY home renovation project.") |
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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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*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 | 4 | 33.7092 | 111 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| True | 138 | |
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| False | 58 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (3, 3) |
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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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| 0.0007 | 1 | 0.2132 | - | |
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| 0.0354 | 50 | 0.1508 | - | |
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| 0.0708 | 100 | 0.0193 | - | |
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| 0.1062 | 150 | 0.0075 | - | |
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| 0.1415 | 200 | 0.0025 | - | |
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| 0.1769 | 250 | 0.0009 | - | |
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| 0.2123 | 300 | 0.0003 | - | |
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| 0.2477 | 350 | 0.0005 | - | |
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| 0.2831 | 400 | 0.0004 | - | |
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| 0.3185 | 450 | 0.0004 | - | |
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| 0.3539 | 500 | 0.0002 | - | |
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| 0.3892 | 550 | 0.0004 | - | |
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| 0.4246 | 600 | 0.0001 | - | |
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| 0.4600 | 650 | 0.0003 | - | |
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| 0.4954 | 700 | 0.0001 | - | |
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| 0.5308 | 750 | 0.0001 | - | |
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| 0.5662 | 800 | 0.0001 | - | |
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| 0.6016 | 850 | 0.0002 | - | |
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| 0.6369 | 900 | 0.0001 | - | |
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| 0.6723 | 950 | 0.0001 | - | |
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| 0.7077 | 1000 | 0.0001 | - | |
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| 0.7431 | 1050 | 0.0 | - | |
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| 0.7785 | 1100 | 0.0001 | - | |
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| 0.8139 | 1150 | 0.0001 | - | |
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| 0.8493 | 1200 | 0.0001 | - | |
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| 0.8846 | 1250 | 0.0001 | - | |
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| 0.9200 | 1300 | 0.0001 | - | |
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| 0.9554 | 1350 | 0.0001 | - | |
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| 0.9908 | 1400 | 0.0001 | - | |
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| **1.0** | **1413** | **-** | **0.017** | |
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| 1.0262 | 1450 | 0.0001 | - | |
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| 1.0616 | 1500 | 0.0001 | - | |
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| 1.0970 | 1550 | 0.0 | - | |
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| 1.1323 | 1600 | 0.0001 | - | |
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| 1.1677 | 1650 | 0.0001 | - | |
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| 1.2031 | 1700 | 0.0001 | - | |
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| 1.2385 | 1750 | 0.0 | - | |
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| 1.2739 | 1800 | 0.0001 | - | |
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| 1.3093 | 1850 | 0.0 | - | |
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| 1.3447 | 1900 | 0.0 | - | |
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| 1.3800 | 1950 | 0.0 | - | |
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| 1.4154 | 2000 | 0.0 | - | |
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| 1.4508 | 2050 | 0.0 | - | |
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| 1.4862 | 2100 | 0.0 | - | |
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| 1.5216 | 2150 | 0.0 | - | |
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| 1.5570 | 2200 | 0.0 | - | |
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| 1.5924 | 2250 | 0.0 | - | |
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| 1.6277 | 2300 | 0.0 | - | |
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| 1.6631 | 2350 | 0.0 | - | |
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| 1.6985 | 2400 | 0.0 | - | |
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| 1.7339 | 2450 | 0.0 | - | |
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| 1.7693 | 2500 | 0.0 | - | |
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| 1.8047 | 2550 | 0.0 | - | |
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| 1.8401 | 2600 | 0.0 | - | |
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| 1.8754 | 2650 | 0.0 | - | |
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| 1.9108 | 2700 | 0.0001 | - | |
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| 1.9462 | 2750 | 0.0 | - | |
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| 1.9816 | 2800 | 0.0 | - | |
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| 2.0 | 2826 | - | 0.018 | |
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| 2.0170 | 2850 | 0.0 | - | |
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| 2.0524 | 2900 | 0.0 | - | |
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| 2.0878 | 2950 | 0.0 | - | |
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| 2.1231 | 3000 | 0.0 | - | |
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| 2.1585 | 3050 | 0.0 | - | |
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| 2.1939 | 3100 | 0.0 | - | |
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| 2.2293 | 3150 | 0.0 | - | |
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| 2.2647 | 3200 | 0.0 | - | |
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| 2.3001 | 3250 | 0.0 | - | |
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| 2.3355 | 3300 | 0.0 | - | |
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| 2.3708 | 3350 | 0.0 | - | |
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| 2.4062 | 3400 | 0.0 | - | |
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| 2.4416 | 3450 | 0.0 | - | |
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| 2.4770 | 3500 | 0.0 | - | |
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| 2.5124 | 3550 | 0.0 | - | |
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| 2.5478 | 3600 | 0.0 | - | |
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| 2.5832 | 3650 | 0.0 | - | |
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| 2.6185 | 3700 | 0.0 | - | |
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| 2.6539 | 3750 | 0.0 | - | |
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| 2.6893 | 3800 | 0.0 | - | |
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| 2.7247 | 3850 | 0.0 | - | |
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| 2.7601 | 3900 | 0.0 | - | |
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| 2.7955 | 3950 | 0.0 | - | |
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| 2.8309 | 4000 | 0.0 | - | |
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| 2.8662 | 4050 | 0.0001 | - | |
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| 2.9016 | 4100 | 0.0 | - | |
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| 2.9370 | 4150 | 0.0 | - | |
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| 2.9724 | 4200 | 0.0001 | - | |
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| 3.0 | 4239 | - | 0.0182 | |
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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: 2.7.0 |
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- Transformers: 4.40.0 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.19.0 |
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- Tokenizers: 0.19.1 |
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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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