PRATIK C comments

Results 44 comments of


                                            PRATIK C

Regarding flair/ner-english-ontonotes model fine tuning

> > Am I missing something? > > I think you are missing, that Flair creates Flair-models and not hugging face models. You can see how to use them by...

Avoid model overfitting

Any answer on this. How to avoid overfitting on smaller data-points. Is `dropout` only option?

Guide for NER Augmentation

Hi @makcedward @manishiitg , any recent improvements to create `NER synthetic data`. Original_text=`My name is Pratik. I live in India' Augmented can be: 1. `My name is Jon. I live...

How to correct TypeError: zip argument #1 must support iteration training in multiple GPU

> Hi @pratikchhapolika, without a full traceback of the errors, we can't tell if this is a `transfomers` issue or a issue in your custom modeling code. This is the...

Model files are big?

Hi @jon-tow @python273 Why we have multiple .bin files inside `stabilityai/stablelm-base-alpha-7b`? When we load the model which bin file is loaded?

Is this correct approach to do Prompt Tuning on DollyV2 model

> What GPU are you running on? eos_token looks wrong in your last snippet. Can you just use the provided pipeline? I don't think you need an attention mask either....

Is this correct approach to do Prompt Tuning on DollyV2 model

> trainer.train() @srowen I am doing Prompt-Tuning and training the model using `Trainer API` and then saving model in local using `trainer.save_model("dolly3b_demo_model")`. Do I need to upload in Hugging face?...

Is this correct approach to do Prompt Tuning on DollyV2 model

Hi @srowen , any help on this?

repr CRF causes AttributeError in scikit-learn 0.24.0 (or later)

How to fix?

Getting nan(not a number) when evaluating faithfulness

How to modify the default prompt here, using the above scenario and example: ``` from langchain_core.callbacks import BaseCallbackHandler class TestCallback(BaseCallbackHandler): def on_llm_start(self, serialized, prompts, **kwargs): print(f"Prompts: {prompts}\n\n") def on_llm_end(self, response,...