
This model has been fine-tuned on the novels written by H G Wells. H G Wells is a famous author and is well known for his science fiction novels. He is known as the father of science fiction. 

This model can be used to generate text in the style of H G Wells. Since this model has been trained on most novels of the science fiction genre by H G Wells, it produces text of the science fiction genre.

The limitations of this model are that it can only generate text in the style of H G Wells, and not in the style of any other author. It may also be prone to generate text that has the stereotypes that were present at that time. An ethical consideration that needs to be taken into account is that the generated text may have gender biases that were present at the time when H G Wells wrote these novels.

I created my own dataset to train this model. I chose 14 novels written by H G Wells for my dataset. Most of the novels in the dataset are of the genre science fiction. The dataset contains more than 1 million tokens.

The evaluation results are good. The model is able to generate text in the style of H G Wells. Most of the text generated is of the science fiction genre.

The texts included in the corpus are novels written by H G Wells. The novels in the corpus are:

The Time Machine - 37677

In the Days of the Comet - 95299

The Food of the Gods - 90723

Tales of Space and Time - 85850

The World Set Free - 74971

The War of the Worlds - 69530

The First Men in the Moon - 81517

The Invisible Man - 60581

The Island of Doctor Moreau - 52073

The Sleeper Awakes - 91274

The War in the Air - 115573

The Research Magnificient - 131866

The Udying Fire - 52036

The Red Room - 4618

The total number of tokens in the corpus is 1043588.

The corpus was created by downloading and combining 14 novels of the famous author H G Wells from Project Gutenberg. Most of these novels are science fiction novels, so this model has been trained to generate text of the science fiction genre. It has been trained to produce text in the style of H G Wells. 
This model was created on 23rd February, 2023.

The corpus consists of 14 novels written by H G Wells downloaded from Project Gutenberg. The text added by Project Gutenberg at the beginning and end of each novel was removed. Then the entire text in each novel 
was converted into one line. Then the single line was broken into 20 parts. In this way 20 lines were generated for each novel. The lines from each novel were then combined and 
stored in a single text file. This is the preprocessing that was done on the text files. The text was tokenized by using the GPT2Tokenizer from the transformers library. This text file was then used to finetune the model. 

The values of the hyperparameters used during finetuning are:

batch_size = 2

max length = 1024

epochs = 10

learning rate = 5e-4

warmup steps = 1e2

epsilon = 1e-8


Training Loss: 2.26

Training Perplexity: 9.57

Validation Loss: 3.84

Validation Perplexity: 46.43

The corpus has been uploaded on HuggingFace. It can be accessed from the following link: https://huggingface.co/datasets/MinzaKhan/HGWells


{"language": ["en"], "tags": ["science fiction", "text generation"], "pipeline_tag": "text-generation"}
transformers pytorch gpt2 text-generation science fiction text generation en autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #science fiction #text generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


This model has been fine-tuned on the novels written by H G Wells. H G Wells is a famous author and is well known for his science fiction novels. He is known as the father of science fiction. 

This model can be used to generate text in the style of H G Wells. Since this model has been trained on most novels of the science fiction genre by H G Wells, it produces text of the science fiction genre.

The limitations of this model are that it can only generate text in the style of H G Wells, and not in the style of any other author. It may also be prone to generate text that has the stereotypes that were present at that time. An ethical consideration that needs to be taken into account is that the generated text may have gender biases that were present at the time when H G Wells wrote these novels.

I created my own dataset to train this model. I chose 14 novels written by H G Wells for my dataset. Most of the novels in the dataset are of the genre science fiction. The dataset contains more than 1 million tokens.

The evaluation results are good. The model is able to generate text in the style of H G Wells. Most of the text generated is of the science fiction genre.

The texts included in the corpus are novels written by H G Wells. The novels in the corpus are:

The Time Machine - 37677

In the Days of the Comet - 95299

The Food of the Gods - 90723

Tales of Space and Time - 85850

The World Set Free - 74971

The War of the Worlds - 69530

The First Men in the Moon - 81517

The Invisible Man - 60581

The Island of Doctor Moreau - 52073

The Sleeper Awakes - 91274

The War in the Air - 115573

The Research Magnificient - 131866

The Udying Fire - 52036

The Red Room - 4618

The total number of tokens in the corpus is 1043588.

The corpus was created by downloading and combining 14 novels of the famous author H G Wells from Project Gutenberg. Most of these novels are science fiction novels, so this model has been trained to generate text of the science fiction genre. It has been trained to produce text in the style of H G Wells. 
This model was created on 23rd February, 2023.

The corpus consists of 14 novels written by H G Wells downloaded from Project Gutenberg. The text added by Project Gutenberg at the beginning and end of each novel was removed. Then the entire text in each novel 
was converted into one line. Then the single line was broken into 20 parts. In this way 20 lines were generated for each novel. The lines from each novel were then combined and 
stored in a single text file. This is the preprocessing that was done on the text files. The text was tokenized by using the GPT2Tokenizer from the transformers library. This text file was then used to finetune the model. 

The values of the hyperparameters used during finetuning are:

batch_size = 2

max length = 1024

epochs = 10

learning rate = 5e-4

warmup steps = 1e2

epsilon = 1e-8


Training Loss: 2.26

Training Perplexity: 9.57

Validation Loss: 3.84

Validation Perplexity: 46.43

The corpus has been uploaded on HuggingFace. It can be accessed from the following link: URL


TAGS
#transformers #pytorch #gpt2 #text-generation #science fiction #text generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #gpt2 #text-generation #science fiction #text generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
