# Saving Models in Active Learning setting **URL:** **Author:** ![Nitish1295](https://avatars.discourse-cdn.com/v4/letter/n/ad7895/32.png) [@Nitish1295](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FNitish1295)%5C **Post date:** [November 22, 2022, 8:36am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fsaving-models-in-active-learning-setting%2F26493%2F1 "2022-11-22T08:36:08Z") Some context, currently I am running an active learning loop. Initially I enable dropouts for my bert model while making predictions to gather uncertainties and to select the most uncertain samples. Now after doing that I fine tune my model based on the **initial weights from HF** along with new samples I have gathered from the active learning loop. The AL loop looks something like the following: 1. Fine tune model 2. Enable dropouts while making predictions(MCdropout) and gather uncertainties 3. Save Model if needed 4. Select most informative and uncertain points based on point 2 and add to train and remove from pool 5. Initialize the model weights from the actual HF model and go to step 1 to run fine tune again and so on Now in step 3 when I try to save model as `trainer.save_model('path/to/model_dir)` does that ensure that my model is being saved in a `model.eval()` state. My assumption was that when I save the model it would be saved in the train state with dropouts enabled( **since I had manually enabled dropouts and never switched them off** ) but that is not the case. When I try to load the model it is no longer in training state(**Which is essentially what I want, but I want to make sure that this is the expected behavior out of trainer.save() or is something off**) ```python from transformers import AutoModelForSequenceClassification from transformers import TextClassificationPipeline import os model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5) # Fine tune model trainer.train() # Explicityly Enable dropouts in prediction(MCdropuout) for m in model.modules(): if m. __class__. __name__.startswith('Dropout'): m.train() print(f"Droputs enabled: {model.dropout.training}") # True here # Save model trainer.save_model(os.path.join(os.getcwd(),'model')) # Suppose now you load this model at some later point in time loaded_model = AutoModelForSequenceClassification.from_pretrained('model') print(f"Droputs enabled: {loaded_model.dropout.training}") # False here, which is perfect. But is this expected? ``` The logs are much longer and this is not the actual code but the above code is the general idea. It is fine tuning docs from HF [Fine-tune a pretrained model](https://huggingface.co/docs/transformers/training). You might be tempted to conclude that I can just save my model before I enable dropouts for predictions, but think of this running in a loop. At the nth loop where n!=0(since active learning would not work best at your first pass), dropouts are already enabled for predictions. **To be more precise** - Looking for inputs on the behavior of trainer.save() in the above scenario. While saving the model with all dropouts enabled what would/should trainer.save() do? - Based on the above question, do you suggest that I must disable dropouts and then save the model ? If yes then how, since I only know about trainer.save() which will save my trained model. Please suggest if there are any alternative methods to save **Some References I have already checked** - [How to save my model to use it later](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-save-my-model-to-use-it-later%2F20568) ---
**Author:** ![Nitish1295](https://avatars.discourse-cdn.com/v4/letter/n/ad7895/32.png) [@Nitish1295](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FNitish1295)%5C **Post date:** [December 6, 2022, 9:30am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fsaving-models-in-active-learning-setting%2F26493%2F2 "2022-12-06T09:30:37Z")
Any ideas for this question? 👀 Please let me know if I need to add more context/clarity to this question.