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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## Citation
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- **Papers:** Vanin
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- **Developed by:** @myentity, @VadZhen, @Alek123
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- **License:** MIT
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**BibTeX:**
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## Citation
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- **Papers:** Vanin A, Bolshev V and Panfilova A (2025) Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application. Front. Psychiatry 16:1608163. doi: 10.3389/fpsyt.2025.1608163. <https://doi.org/10.3389/fpsyt.2025.1608163>
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- **Developed by:** @myentity, @VadZhen, @Alek123
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- **License:** MIT
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**BibTeX:**
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```
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@ARTICLE{10.3389/fpsyt.2025.1608163,
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AUTHOR={Vanin, Alexander and Bolshev, Vadim and Panfilova, Anastasia },
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TITLE={Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application},
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JOURNAL={Frontiers in Psychiatry},
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VOLUME={Volume 16 - 2025},
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YEAR={2025},
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URL={https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1608163},
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DOI={10.3389/fpsyt.2025.1608163},
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ISSN={1664-0640},
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```
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