--- language: - en license: gpl tags: - autograding - essay quetion - sentence similarity metrics: - accuracy library_name: peft datasets: - mohamedemam/Essay-quetions-auto-grading --- # Model Card for Model ID fine tuned version of Mistral on Essay-quetions-auto-grading ### Model Description We are thrilled to introduce our graduation project, the EM2 model, designed for automated essay grading in both Arabic and English. 📝✨ To develop this model, we first created a custom dataset for training. We adapted the QuAC and OpenOrca datasets to make them suitable for our automated essay grading application. Our model utilizes the following impressive models: Mistral: 96% LLaMA: 93% FLAN-T5: 93% BLOOMZ (Arabic): 86% MT0 (Arabic): 84% You can try our models for auto-grading on Hugging Face! 🌐 We then deployed these models for practical use. We are proud of our team's hard work and the potential impact of the EM2 model in the field of education. 🌟 #MachineLearning #AI #Education #EssayGrading #GraduationProject - **Developed by:** mohamed emam - **Model type:** decoder only - **Language(s) (NLP):** English - **License:** gpl - **Finetuned from model :** Mistral - **Repository:** https://github.com/mohamed-em2m/Automatic-Grading-AI - ### Direct Use auto grading for essay quetions ### Explain how it work - model take three inputs first context or perfect answer + quetion on context + student answer then model output the result ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6456f2eca9b8e1fd4cbe5ebe/_O75HT2zb2TYZOEkX4YXO.png) ### Training Data - **mohamedemam/Essay-quetions-auto-grading-arabic** ### Training Procedure using Trl ### Pipline ```python from transformers import Pipeline import torch.nn.functional as F class MyPipeline: def __init__(self,model,tokenizer): self.model=model self.tokenizer=tokenizer def chat_Format(self,context, quetion, answer): return "Instruction:/n check answer is true or false of next quetion using context below:\n" + "#context: " + context + f".\n#quetion: " + quetion + f".\n#student answer: " + answer + ".\n#response:" def __call__(self, context, quetion, answer,generate=1,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1): inp=self.chat_Format(context, quetion, answer) w = self.tokenizer(inp, add_special_tokens=True, pad_to_max_length=True, return_attention_mask=True, return_tensors='pt') response="" if(generate): outputs = self.tokenizer.batch_decode(self.model.generate(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda(), max_new_tokens=max_new_tokens, num_beams=num_beams, do_sample=do_sample, num_return_sequences=num_return_sequences), skip_special_tokens=True) response = outputs s =self.model(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda())['logits'][0][-1] s = F.softmax(s, dim=-1) yes_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize("True")[0]) no_token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize("False")[0]) for i in ["Yes", "yes", "True", "true","صحيح"]: for word in self.tokenizer.tokenize(i): s[yes_token_id] += s[self.tokenizer.convert_tokens_to_ids(word)] for i in ["No", "no", "False", "false","خطأ"]: for word in self.tokenizer.tokenize(i): s[no_token_id] += s[self.tokenizer.convert_tokens_to_ids(word)] true = (s[yes_token_id] / (s[no_token_id] + s[yes_token_id])).item() return {"response": response, "true": true} 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: Model Selection: Choosing a pre-trained model that fits the application's needs. Infrastructure Setup: Setting up the necessary hardware and software infrastructure to run the model efficiently, including cloud services, GPUs, and necessary libraries. Integration: Integrating the model into an application, which can involve setting up APIs or embedding the model directly into the software. Optimization: Fine-tuning the model for specific tasks or domains and optimizing it for performance and cost-efficiency. Monitoring and Maintenance: Ensuring the model performs well over time, monitoring for biases, and updating the model as needed.""" quetion="What are the key considerations when choosing a cloud service provider for deploying a large language model like GPT-4?" answer="""When choosing a cloud service provider for deploying a large language model like GPT-4, the key considerations include: Compute Power: Ensure the provider offers high-performance GPUs or TPUs capable of handling the computational requirements of the model. Scalability: The ability to scale resources up or down based on the application's demand to handle varying workloads efficiently. Cost: Analyze the pricing models to understand the costs associated with compute time, storage, data transfer, and any other services. 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. Security and Compliance: Ensure the provider adheres to industry standards for security and compliance, protecting sensitive data and maintaining privacy. Latency and Availability: Consider the geographical distribution of data centers to ensure low latency and high availability for your end-users. By 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.""" from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM,AutoTokenizer config = PeftConfig.from_pretrained("mohamedemam/Em2-llama-7b") base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "mohamedemam/Em2-llama-7b") tokenizer = AutoTokenizer.from_pretrained("mohamedemam/Em2-llama-7b", trust_remote_code=True) 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} ### 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