Update README.md

README.md

@@ -1,21 +1,29 @@
-
-
+---
 language:
-  - en
+- en
 license: gpl
 tags:
-  - autograding
-  - essay quetion
-  - sentence similarity
+- autograding
+- essay quetion
+- sentence similarity
 metrics:
-  - accuracy
+- accuracy
 library_name: peft
 datasets:
-  - mohamedemam/Essay-quetions-auto-grading
+- mohamedemam/Essay-quetions-auto-grading
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
 
-Model Card for Model ID
-Model Details
-Model Description
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
 
 We are thrilled to introduce our graduation project, the EM5 model, designed for automated essay grading in both Arabic and English. 📝✨
 
@@ -23,7 +31,11 @@ To develop this model, we first created a custom dataset for training. We adapte
 
 Our model utilizes the following impressive models:
 
-Mistral: 96% LLaMA: 93% FLAN-T5: 93% BLOOMZ (Arabic): 86% MT0 (Arabic): 84%
+Mistral: 96%
+LLaMA: 93%
+FLAN-T5: 93%
+BLOOMZ (Arabic): 86%
+MT0 (Arabic): 84%
 
 You can try our models for auto-grading on Hugging Face! 🌐
 
@@ -31,31 +43,39 @@ We then deployed these models for practical use. We are proud of our team's hard
 
 #MachineLearning #AI #Education #EssayGrading #GraduationProject
 
-    Developed by: mohamed emam
-    Model type: decoder only
-    Language(s) (NLP): English
-    License: gpl
-    Finetuned from model : llama
+- **Developed by:** mohamed emam
+- **Model type:** decoder only
+- **Language(s) (NLP):** English
+- **License:** gpl
+- **Finetuned from model :** llama
+
+
+<!-- Provide the basic links for the model. -->
 
-    Repository: https://github.com/mohamed-em2m/auto-grading
+- **Repository:** https://github.com/mohamed-em2m/auto-grading
+### Direct Use
 
-Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 auto grading for essay quetions
-Downstream Use [optional]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 Text generation
 
 [More Information Needed]
-Training Data
 
-    mohamedemam/Essay-quetions-auto-grading-arabic
+### Training Data
+- **mohamedemam/Essay-quetions-auto-grading-arabic**
 
-Training Procedure
 
-using Trl
-Pipline
+### Training Procedure
 
+using Trl
+### Pipline
+```python
 from transformers import Pipeline
 import torch.nn.functional as F
 
@@ -120,52 +140,50 @@ base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf")
 model = PeftModel.from_pretrained(base_model, "mohamedemam/Em2-llama-7b")
 pipe=MyPipeline(model,tokenizer)
 print(pipe(context,quetion,answer,generate=True,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1))
+```
+- **output:**{'response': ["Instruction:/n check answer is true or false of next quetion using context below:\n#context: Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps:\n\n    Model Selection: Choosing a pre-trained model that fits the application's needs.\n    Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries.\n    Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software.\n    Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency.\n    Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed..\n#quetion: What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?.\n#student answer: When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include:\n    Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model.\n    Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently.\n    Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services.\n    Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation.\n    Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy.\n    Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users.\n\nBy evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model..\n#response:  true the answer is"], 'true': 0.943033754825592}
 
-    output:{'response': ["Instruction:/n check answer is true or false of next quetion using context below:\n#context: Large language models, such as GPT-4, are trained on vast amounts of text data to understand and generate human-like text. The deployment of these models involves several steps:\n\n Model Selection: Choosing a pre-trained model that fits the application's needs.\n Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries.\n Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software.\n Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency.\n Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed..\n#quetion: What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?.\n#student answer: When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include:\n Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model.\n Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently.\n Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services.\n Integration and Support: Availability of tools and libraries that support easy integration of the model into your applications, along with robust technical support and documentation.\n Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy.\n Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users.\n\nBy evaluating these factors, you can select a cloud service provider that aligns with your deployment needs, ensuring efficient and cost-effective operation of your large language model..\n#response: true the answer is"], 'true': 0.943033754825592}
-
-Chat Format Function
-
+### Chat Format Function
 This function formats the input context, question, and answer into a specific structure for the model to process.
 
+```python
 def chat_Format(self, context, question, answer):
     return "Instruction:/n check answer is true or false of next question using context below:\n" + "#context: " + context + f".\n#question: " + question + f".\n#student answer: " + answer + ".\n#response:"
-
-Configuration
-Dropout Probability for LoRA Layers
-
-    lora_dropout: 0.05
-
-Quantization Settings
-
-    use_4bit: True
-    bnb_4bit_compute_dtype: "float16"
-    bnb_4bit_quant_type: "nf4"
-    use_nested_quant: False
-
-Output Directory
-
-    output_dir: "./results"
-
-Training Parameters
-
-    num_train_epochs: 1
-    fp16: False
-    bf16: False
-    per_device_train_batch_size: 1
-    per_device_eval_batch_size: 4
-    gradient_accumulation_steps: 8
-    gradient_checkpointing: True
-    max_grad_norm: 0.3
-    learning_rate: 5e-5
-    weight_decay: 0.001
-    optim: "paged_adamw_8bit"
-    lr_scheduler_type: "constant"
-    max_steps: -1
-    warmup_ratio: 0.03
-    group_by_length: True
-
-Logging and Saving
-
-    save_steps: 100
-    logging_steps: 25
-    max_seq_length: False
+```
+
+
+## Configuration
+
+### Dropout Probability for LoRA Layers
+- **lora_dropout:** 0.05
+
+### Quantization Settings
+- **use_4bit:** True
+- **bnb_4bit_compute_dtype:** "float16"
+- **bnb_4bit_quant_type:** "nf4"
+- **use_nested_quant:** False
+
+### Output Directory
+- **output_dir:** "./results"
+
+### Training Parameters
+- **num_train_epochs:** 1
+- **fp16:** False
+- **bf16:** False
+- **per_device_train_batch_size:** 1
+- **per_device_eval_batch_size:** 4
+- **gradient_accumulation_steps:** 8
+- **gradient_checkpointing:** True
+- **max_grad_norm:** 0.3
+- **learning_rate:** 5e-5
+- **weight_decay:** 0.001
+- **optim:** "paged_adamw_8bit"
+- **lr_scheduler_type:** "constant"
+- **max_steps:** -1
+- **warmup_ratio:** 0.03
+- **group_by_length:** True
+
+### Logging and Saving
+- **save_steps:** 100
+- **logging_steps:** 25
+- **max_seq_length:** False