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richie-ghost
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setfit-mental-bert-base-uncased-MH-Topic-Check

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +292 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +7 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +62 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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README.md ADDED
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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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+
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+ # SetFit with mental/mental-bert-base-uncased
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+
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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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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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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+
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+ ### Model Sources
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+
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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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+
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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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+
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+ ## Evaluation
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+
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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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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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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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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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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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+
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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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+
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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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+
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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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+ | 0.6369 | 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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+ | 1.6631 | 2350 | 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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+
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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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+
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+ ## Citation
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+
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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},
266
+ 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},
269
+ 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}
273
+ }
274
+ ```
275
+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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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.*
286
+ -->
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+
288
+ <!--
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+ ## Model Card Contact
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+
291
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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+ "stride": 0,
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+ "strip_accents": null,
57
+ "tokenize_chinese_chars": true,
58
+ "tokenizer_class": "BertTokenizer",
59
+ "truncation_side": "right",
60
+ "truncation_strategy": "longest_first",
61
+ "unk_token": "[UNK]"
62
+ }
vocab.txt ADDED
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