hyperparameter search requirements/gpt2 metric
I am trying to fine tune gpt2 to respond to certain prompts in a specific way, so I am training it on strings like 
prompt + "someDivider" + output

I have about 1300 samples of training data, and I wanted to use hyperparameter_search to pick decent hyperparameters. I'm not sure if this requires a validation set, and if it does, what metric do I have to put? Do I even need a metric?

I'm also not sure what to do with the output of hyperparameter_search 

I've tried doing research but I haven't really gotten far on this issue. I am relatively new to training AI's.

- ray/raytune: @richardliaw, @amogkam
- gpt2: @patrickvonplaten, @LysandreJik

```
import pandas as pd
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup,AutoTokenizer, DataCollatorForLanguageModeling, AutoConfig, Trainer, TrainingArguments,AutoModelForCausalLM
from tqdm import tqdm, trange
import torch.nn.functional as F
import csv
from datasets import load_dataset,load_metric
import io
from google.colab import files

#version of gpt we use
model_version = 'gpt2-medium'

#create the dataset
raw_datasets = load_dataset('csv', data_files=['train.csv'])

print(raw_datasets)
print(raw_datasets["train"][1])


#initialize tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained(model_version)
tokenizer.pad_token = tokenizer.unk_token #prevents error where there is no token. 

#function called by trainer to initialize model. I'm doing it this way so the hyperparameters can be tuned
def model_init():
    return GPT2LMHeadModel.from_pretrained(model_version)
    
#helper for tokenizing everything
def tokenize_function(examples):
    return tokenizer(examples["triplet"], truncation=True)

#tokenize all our data
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)

#gets rid of original string data
tokenized_datasets=tokenized_datasets.remove_columns(["triplet"])

print(tokenized_datasets)
print(tokenized_datasets["train"]["input_ids"][1])

#collate data
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

#training args (you can control hyperprarameters from here)
training_args = TrainingArguments(output_dir="Finetuned",
                                  overwrite_output_dir=True,
                                  prediction_loss_only=True, #TODO if you are gonna do a validation set and compute metrics, this must be False but since we arent, I set to true
                                  )

trainer = Trainer(
    model_init=model_init,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    data_collator=data_collator,
    tokenizer=tokenizer,
)

# Automatically finds good hyperparameters, you can pass arguments into it but idk if I want to mess with them rn
trainer.hyperparameter_search()

trainer.train()
trainer.save_model()
```

It seems like if I give it an evaluation set from the training set, and no metric, and then set the parameter search to minimize, it will minimize loss?

And then I just initialize a new trainer in the same script using those hyperparamets?

```
best = trainer.hyperparameter_search(direction="minimize", hp_space=my_hp_space_ray)

hyperParameter = best.hyperparameters
print(best)
print(hyperParameter)

#train using best hyperparameters
training_args = TrainingArguments(output_dir="Finetuned",
                                  overwrite_output_dir=True,
                                  eval_steps=100, #steps before we run eval
                                  disable_tqdm=True,
                                  prediction_loss_only = True, #get rid of this if we end up adding metrics
                                  learning_rate = hyperParameter["learning_rate"],
                                  num_train_epochs = hyperParameter["num_train_epochs"],
                                  seed = hyperParameter["seed"],
                                  per_device_train_batch_size =hyperParameter["per_device_train_batch_size"],
                                  )
trainer = Trainer(...)
```

Although that seems that I would be effectively running the whole train loop 21 times

edit: just using validation loss doesn't seem to produce good results<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>Hey @MarcM0, 

Could you please use the [forum](https://discuss.huggingface.co/) for such questions. I think for hyperparameter tuning questions that are not clear bugs the forum is the place to post questions :-) <|||||>Ya I figured it out, thank you<|||||>@MarcM0 have you figured out how to set compute_metrics for hyperparameter search for GPT models?<|||||>I just ended up using the default loss<|||||>Thank you @MarcM0. Do you mean you set compute_metrics=None (None was the default value, unless you provide your implementation)?<|||||>I believe so