Update README

README.md

@@ -9,25 +9,79 @@ model-index:
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-# fiction_story_generator
+# GPT-Neo for Fiction Story Generation
+
+This model is a fine-tuned version of EleutherAI's GPT-Neo-125M model, optimized for generating fiction stories. 
+It has been trained on the dataset available at https://github.com/facebookresearch/fairseq/tree/main/examples/stories.
 
-This model is a fine-tuned version of [Tincando/fiction_story_generator](https://huggingface.co/Tincando/fiction_story_generator) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 3.1357
 
 ## Model description
 
-More information needed
+- Model Name: GPT-Neo-Fiction
+- Fine-Tuned By: [Tin/Tincando, Darko Etinger]
+- Model Version: 1.0
 
 ## Intended uses & limitations
 
-More information needed
+The model is designed for creative fiction story generation. It can be used for various purposes, including but not limited to:
+
+- Storytelling: Generate engaging and imaginative fiction stories.
+- Content Generation: Create content for blogs, websites, or other media with a storytelling element.
+- Creative Writing: Assist authors and writers in brainstorming ideas and expanding narratives.
+
+## Model Performance
+
+- Training Data: The model was trained on a diverse dataset of fiction stories.
+- Evaluation Metrics: Performance metrics, such as perplexity or BLEU scores, may vary depending on the specific task and dataset.
+
+## Limitations
+
+- Content Quality: While GPT-Neo-Fiction can generate creative stories, the quality and coherence of the output may vary, and it may occasionally produce nonsensical or inappropriate content.
+- Bias: The model may exhibit biases present in the training data, and it is essential to be cautious when using it for sensitive topics or content.
+- Length of Output: The model may generate text with variable length, and it may not always produce the desired length of output.
+- Fine-Tuning Data: The quality of generated stories is dependent on the quality and diversity of the fine-tuning dataset.
+
+## Usage
+
+```
+from transformers import GPTNeoForCausalLM, GPT2Tokenizer
+
+tokenizer = GPT2Tokenizer.from_pretrained(Tincando/fiction_story_generator)
+model = GPTNeoForCausalLM.from_pretrained(Tincando/fiction_story_generator)
+
+# Generate a fiction story
+input_prompt = "[WP] I can't believe I died the same way twice."
+input_ids = tokenizer(input_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+output = model.generate(input_ids,
+        max_length=300,
+        temperature=0.9,
+        top_k=2,
+        top_p=0.9,
+        repetition_penalty=1.2,
+        do_sample=True,
+        num_return_sequences=2
+)
+
+generated_story = tokenizer.batch_decode(output,clean_up_tokenization_spaces=True)[0]
+print(generated_story)
+```
+
+## Ethical Considerations
+
+When using this model, consider the following ethical guidelines:
 
-## Training and evaluation data
+- Content Moderation: Implement content moderation to ensure that the generated stories do not violate guidelines or community standards.
+- Bias and Fairness: Be aware of potential biases in the model's output and take steps to mitigate them.
+- Privacy: Avoid using personal or sensitive information as input prompts.
+- Legal Compliance: Ensure that the generated content complies with copyright and intellectual property laws.
 
-More information needed
+##Citation
+If you use GPT-Neo-Fiction in your work, please consider citing the original GPT-Neo model and the dataset used for fine-tuning:
 
-## Training procedure
+- [GPT-Neo Paper](https://github.com/EleutherAI/gpt-neo)
+- [Fairseq Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/stories)
+- [Hierarchical Neural Story Generation](https://arxiv.org/abs/1805.04833)
 
 ### Training hyperparameters