Instructions to use mamakos/CMClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamakos/CMClassifier with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "mamakos/CMClassifier") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: teknium/OpenHermes-2.5-Mistral-7B | |
| # Model Card for Model ID | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| ## Model Description | |
| This model is a classifier of closed-mindedness: the unwillingness to have one's knowledge confronted by alternative opinions or inconsistent evidence. | |
| - **Developed by:** Michalis Mamakos https://www.michalismamakos.com/ | |
| - **Paper:** TBA | |
| - **Validation:** TBA | |
| ```python | |
| # install the module | |
| pip install closedmindedness | |
| ``` | |
| ```python | |
| # import and use the module to estimate the probability | |
| # a text displays closed-mindedness | |
| import closedmindedness | |
| model = closedmindedness.classifier() | |
| text_1 = "Nothing can make me change my mind." | |
| text_2 = "Let's discuss this." | |
| text_3 = "I went to the movies yesterday." | |
| texts = [text_1, text_2, text_3] | |
| probs = model.predict(texts) | |
| print(probs) # [9.71055567e-01 1.70469284e-05 3.33786011e-06] | |
| prob = model.predict(text_1) | |
| print(prob) # [0.97105557] | |
| ``` | |
| ## Citation | |
| If you use this model in your research, please cite: TBA | |
| ### Framework versions | |
| - PEFT 0.8.2 |