AIPsy
/

bert-base-therapist-topic-classification-eng

@@ -7,10 +7,8 @@ base_model:
 - google-bert/bert-base-uncased
 ---
-# Model Card for Model ID
 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:
-```
 id2label = {0: 'Time Up and Future Meetings',
             1: 'Complex Emotions Toward Him',
             2: 'Desires and Disappointments',
@@ -51,186 +49,69 @@ id2label = {0: 'Time Up and Future Meetings',
 ```
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-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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-## Uses
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-## How to Get Started with the Model
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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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 ## Model Card Authors [optional]

 - google-bert/bert-base-uncased
 ---
 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:
+```python
 id2label = {0: 'Time Up and Future Meetings',
             1: 'Complex Emotions Toward Him',
             2: 'Desires and Disappointments',
 ```
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")
+model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")
+text = "You know, I mean, it seems like you could just go to work and feel so much better."
+encoding = tokenizer(
+            text,
+            truncation=True,
+            padding="max_length",
+            return_tensors="pt"
+        )
+output = model(encoding['input_ids'], encoding['attention_mask']).logits
+result = np.argmax(output.detach().numpy(), axis=-1)
+print(id2label[result[0]])
+'Job Anxiety and Self-Reflection'
+```
+## Dataset
+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.
+## Links
+- **Paper [optional]:** <https://arxiv.org/abs/2412.17449>
+-
+## Recommendations
+## Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 [More Information Needed]
+## Citation
+**Papers:** Applying LLM and Topic Modelling in Psychotherapeutic Contexts <https://arxiv.org/abs/2412.17449>
+**Developed by:** @myentity
+**License:** MIT
+**Finetuned from model:** google-bert/bert-base-uncased
 **BibTeX:**
+@misc{vanin2024applyingllmtopicmodelling,
+      title={Applying LLM and Topic Modelling in Psychotherapeutic Contexts},
+      author={Alexander Vanin and Vadim Bolshev and Anastasia Panfilova},
+      year={2024},
+      eprint={2412.17449},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2412.17449},
+}
 ## Model Card Authors [optional]