Update README.md

README.md

@@ -14,37 +14,42 @@ should probably proofread and complete it, then remove this comment. -->
 
 # gpt2-medium-finetuned-contract-gen
 
-This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3127
+## Overview
+`gpt2-medium-finetuned-contract-gen` is a model specialized in generating Solidity contract codes. Derived from the [gpt2-medium](https://huggingface.co/gpt2-medium) model by Hugging Face, it's been meticulously trained on an extensive set of Solidity contracts and patterns, making it apt for assisting in drafting or suggesting contract structures.
 
-## Model description
+## Model Description
+This model has been designed specifically for generating Solidity contracts. Being a derivative of the `gpt2-medium` model, it retains the broader capabilities of the parent model while demonstrating a keen proficiency in understanding and generating Solidity-centric texts.
 
-More information needed
+### Performance
+The model reported a loss of `0.3127` on the evaluation set.
 
-## Intended uses & limitations
+## Intended Uses & Limitations
 
-More information needed
+### Intended Uses:
+1. Assist developers by auto-generating contract code snippets based on prompts.
+2. Help in understanding and drafting complex contract structures.
 
-## Training and evaluation data
+### Limitations:
+1. The generated code must be reviewed for security and functional correctness.
+2. The clarity of the generated code largely depends on the specificity of the prompt.
 
-More information needed
+## Training Details
 
-## Training procedure
+### Dataset
+The model was fine-tuned on an undisclosed dataset comprised of a range of Solidity contracts.
 
-### Training hyperparameters
+### Training Hyperparameters:
+- Learning Rate: `5e-05`
+- Train Batch Size: `4`
+- Evaluation Batch Size: `4`
+- Seed: `42`
+- Optimizer: Adam (`betas=(0.9,0.999)`, `epsilon=1e-08`)
+- Learning Rate Scheduler: Cosine with restarts
+- Warmup Steps: `241`
+- Epochs: `4`
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine_with_restarts
-- lr_scheduler_warmup_steps: 241
-- num_epochs: 4
+### Training Results:
 
-### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
@@ -61,9 +66,28 @@ The following hyperparameters were used during training:
 | 0.28          | 2.28  | 11000 | 0.3127          |
 
 
-### Framework versions
+### Dependencies:
+- Transformers: `4.31.0`
+- Pytorch: `2.0.1+cu118`
+- Datasets: `2.14.2`
+- Tokenizers: `0.13.3`
+
+## How to Use
+If you wish to use this model to generate Solidity contract code, follow the steps below:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("ckandemir/gpt2-medium-finetuned-contract-gen")
+model = AutoModelForCausalLM.from_pretrained("ckandemir/gpt2-medium-finetuned-contract-gen")
+
+# Input your code prompt
+input_text = "contract MyToken"
+input_ids = tokenizer.encode(input_text, return_tensors='pt')
+sample_output = model.generate(input_ids, do_sample=True, max_length=400, num_return_sequences=1, temperature=0.7)
+
+# Decode and print the generated text
+generated_text = tokenizer.decode(sample_output[0], skip_special_tokens=True)
+print(generated_text)
 
-- Transformers 4.31.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.2
-- Tokenizers 0.13.3