Text Classification
Transformers
Safetensors
English
bert
PyTorch
multi-class-classification
text-embeddings-inference
Instructions to use AIPsy/bert-base-client-topic-classification-eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIPsy/bert-base-client-topic-classification-eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIPsy/bert-base-client-topic-classification-eng")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-client-topic-classification-eng") model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-client-topic-classification-eng") - Notebooks
- Google Colab
- Kaggle
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library_name: transformers
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---
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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**BibTeX:**
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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library_name: transformers
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license: mit
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language:
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- en
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base_model:
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- google-bert/bert-base-uncased
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pipeline_tag: text-classification
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tags:
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- PyTorch
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- multi-class-classification
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---
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This is Bert-base-uncased model fine-tuned for topic classification of therapist remarks in psychotherapeutic contexts. The task is a multi-class classification with the following labels:
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```python
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id2label = {0: 'Others',
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1: 'Relationship with a Female Figure',
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2: 'Experiencing and Articulating Emotions',
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3: 'Work and Personal Fulfillment',
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4: 'Relationship with a Male Figure',
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5: 'Meaning-Making and Conceptualization',
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6: 'Education and Personal Development',
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7: 'Experience of Fear and Anxiety',
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8: 'Sexual Relationships and Identity',
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9: 'Expression and Communication Difficulties',
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10: 'Self-Acceptance and Social Relationships',
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11: 'Time Perception and Management',
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12: 'Cyclical Patterns of Experience',
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13: 'Therapy Experience and Reflection',
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14: 'Positive Emotional Experience',
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15: 'Mother-Child Relationship Dynamics',
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16: 'Creativity and Emotional Expression',
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17: 'Humor and Cognitive Reframing',
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18: 'Emotional Disturbance and Regulation',
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19: 'Expressions of Certainty',
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20: 'Guilt and Responsibility',
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21: 'Self-Reflection on Maturity',
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22: 'Sleep-Wake Cycle and Daily Rhythm',
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23: 'Life Orientation and Authenticity',
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24: 'Home and Attachment-Autonomy Conflict',
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25: 'Parenting and Family Responsibility',
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26: 'Internal Conflict and Emotional Regulation',
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27: 'Friendship Dynamics',
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28: 'Reflection and Awareness of Unconsidered Thought',
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29: 'Reading as Cognitive and Reflective Engagement',
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30: 'Processes of Personal Change',
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31: 'Emotional Pain and Hurt',
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32: 'Belief, Faith, and Existential Doubt',
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33: 'Evaluation of Neurotic and Healthy States',
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34: 'Emotional Distress and Negative Evaluation',
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35: 'Experience and Regulation of Anger',
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36: 'Positive Emotional Evaluation',
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37: 'Dream Experience and Symbolic Processing',
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38: 'Father-Child Relationship Dynamics',
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39: 'Problem Framing and Self-Resolution',
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40: 'Personal Learning Experiences',
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41: 'Racial Identity and Intergroup Relations',
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42: 'Healthcare Interactions and Medical Authority',
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43: 'Uncertainty about Correctness',
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44: 'Evaluation of Marriage',
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45: 'Marital Relationship and Communication',
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46: 'Self-Worth and Capacity for Love',
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47: 'Self-Expression Through Appearance'}
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```
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-client-topic-classification-eng")
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model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-client-topic-classification-eng")
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text = "You know, I mean, it seems like you could just go to work and feel so much better."
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encoding = tokenizer(
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text,
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truncation=True,
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padding="max_length",
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return_tensors="pt"
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)
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output = model(encoding['input_ids'], encoding['attention_mask']).logits
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result = np.argmax(output.detach().numpy(), axis=-1)
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print(id2label[result[0]])
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'Job Anxiety and Self-Reflection'
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```
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## Dataset
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The source material was the recordings of psychotherapeutic sessions posted on YouTube in the public domain. After conducting speaker diarization and transcription of the recordings 15324 items (sentences) were obtained.
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## Recommendations
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Given the broad context of issues discussed in psychotherapeutic sessions, the authors believe that this model can be used to analyze human communication in general.
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## Metrics
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Score metrics of trained model
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|Sample|F1 macro|
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|:-|:-:|
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|Test|0.76|
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|Validation|0.76|
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|Train|0.99|
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F1 score metrics for test sample across categories
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|ID|Topic|Precision|Recall|F1|
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|:-:|:-|:-:|:-:|:-:|
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| 0 | Others | 0.92 | 0.98 | 0.95 |
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| 1 | Relationship with a Female Figure | 0.94 | 0.94 | 0.94 |
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| 2 | Experiencing and Articulating Emotions | 0.88 | 0.77 | 0.82 |
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| 3 | Work and Personal Fulfillment | 0.71 | 0.72 | 0.72 |
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| 4 | Relationship with a Male Figure | 0.90 | 0.92 | 0.91 |
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| 5 | Meaning-Making and Conceptualization | 0.69 | 0.80 | 0.74 |
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| 6 | Education and Personal Development | 0.84 | 0.85 | 0.85 |
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| 7 | Experience of Fear and Anxiety | 0.82 | 0.84 | 0.83 |
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| 8 | Sexual Relationships and Identity | 0.79 | 0.75 | 0.77 |
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| 9 | Expression and Communication Difficulties | 0.69 | 0.77 | 0.73 |
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| 10 | Self-Acceptance and Social Relationships | 0.67 | 0.69 | 0.68 |
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| 11 | Time Perception and Management | 0.82 | 0.75 | 0.79 |
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| 12 | Cyclical Patterns of Experience | 0.81 | 0.75 | 0.78 |
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| 13 | Therapy Experience and Reflection | 0.79 | 0.79 | 0.79 |
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| 14 | Positive Emotional Experience | 0.72 | 0.67 | 0.70 |
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| 15 | Mother-Child Relationship Dynamics | 0.81 | 0.74 | 0.77 |
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| 16 | Creativity and Emotional Expression | 0.78 | 0.72 | 0.75 |
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| 17 | Humor and Cognitive Reframing | 0.75 | 0.58 | 0.66 |
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| 18 | Emotional Disturbance and Regulation | 0.76 | 0.70 | 0.73 |
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| 19 | Expressions of Certainty | 0.86 | 0.90 | 0.88 |
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| 20 | Guilt and Responsibility | 0.65 | 0.74 | 0.70 |
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| 21 | Self-Reflection on Maturity | 0.64 | 0.65 | 0.64 |
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| 131 |
+
| 22 | Sleep-Wake Cycle and Daily Rhythm | 0.66 | 0.73 | 0.69 |
|
| 132 |
+
| 23 | Life Orientation and Authenticity | 0.62 | 0.64 | 0.63 |
|
| 133 |
+
| 24 | Home and Attachment-Autonomy Conflict | 0.91 | 0.69 | 0.79 |
|
| 134 |
+
| 25 | Parenting and Family Responsibility | 0.83 | 0.73 | 0.78 |
|
| 135 |
+
| 26 | Internal Conflict and Emotional Regulation | 0.74 | 0.80 | 0.77 |
|
| 136 |
+
| 27 | Friendship Dynamics | 0.64 | 0.66 | 0.65 |
|
| 137 |
+
| 28 | Reflection and Awareness of Unconsidered Thought | 0.69 | 0.79 | 0.74 |
|
| 138 |
+
| 29 | Reading as Cognitive and Reflective Engagement | 0.74 | 0.81 | 0.78 |
|
| 139 |
+
| 30 | Processes of Personal Change | 0.69 | 0.61 | 0.65 |
|
| 140 |
+
| 31 | Emotional Pain and Hurt | 0.81 | 0.81 | 0.81 |
|
| 141 |
+
| 32 | Belief, Faith, and Existential Doubt | 0.91 | 0.85 | 0.88 |
|
| 142 |
+
| 33 | Evaluation of Neurotic and Healthy States | 0.62 | 0.77 | 0.69 |
|
| 143 |
+
| 34 | Emotional Distress and Negative Evaluation | 0.66 | 0.69 | 0.67 |
|
| 144 |
+
| 35 | Experience and Regulation of Anger | 0.76 | 0.76 | 0.76 |
|
| 145 |
+
| 36 | Positive Emotional Evaluation | 0.67 | 0.70 | 0.68 |
|
| 146 |
+
| 37 | Dream Experience and Symbolic Processing | 0.83 | 0.88 | 0.86 |
|
| 147 |
+
| 38 | Father-Child Relationship Dynamics | 0.83 | 0.71 | 0.77 |
|
| 148 |
+
| 39 | Problem Framing and Self-Resolution | 0.71 | 0.56 | 0.62 |
|
| 149 |
+
| 40 | Personal Learning Experiences | 0.73 | 0.56 | 0.63 |
|
| 150 |
+
| 41 | Racial Identity and Intergroup Relations | 0.73 | 0.92 | 0.81 |
|
| 151 |
+
| 42 | Healthcare Interactions and Medical Authority | 0.76 | 0.83 | 0.79 |
|
| 152 |
+
| 43 | Uncertainty about Correctness | 0.86 | 0.72 | 0.78 |
|
| 153 |
+
| 44 | Evaluation of Marriage | 0.78 | 0.95 | 0.86 |
|
| 154 |
+
| 45 | Marital Relationship and Communication | 0.81 | 0.85 | 0.83 |
|
| 155 |
+
| 46 | Self-Worth and Capacity for Love | 0.77 | 0.74 | 0.76 |
|
| 156 |
+
| 47 | Self-Expression Through Appearance | 0.68 | 0.63 | 0.65 |
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
## Citation
|
| 161 |
+
|
| 162 |
+
- **Papers:** Vanin, A., Bolshev, V., & Panfilova, A. (2024). Applying LLM and Topic Modelling in Psychotherapeutic Contexts. ArXiv, abs/2412.17449. <https://arxiv.org/abs/2412.17449>
|
| 163 |
+
- **Developed by:** @myentity, @VadZhen, @Alek123
|
| 164 |
+
- **License:** MIT
|
| 165 |
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| 166 |
|
| 167 |
**BibTeX:**
|
| 168 |
|
| 169 |
+
```
|
| 170 |
+
@misc{vanin2024applyingllmtopicmodelling,
|
| 171 |
+
title={Applying LLM and Topic Modelling in Psychotherapeutic Contexts},
|
| 172 |
+
author={Alexander Vanin and Vadim Bolshev and Anastasia Panfilova},
|
| 173 |
+
year={2024},
|
| 174 |
+
eprint={2412.17449},
|
| 175 |
+
archivePrefix={arXiv},
|
| 176 |
+
primaryClass={cs.LG},
|
| 177 |
+
url={https://arxiv.org/abs/2412.17449},
|
| 178 |
+
}
|
| 179 |
+
```
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