| --- |
| 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 |
|
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| fine tuned version of Mistral on Essay-quetions-auto-grading |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| We are thrilled to introduce our graduation project, the EM2 model, designed for automated essay grading in both Arabic and English. 📝✨ |
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| 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. |
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| Our model utilizes the following impressive models: |
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| Mistral: 96% |
| LLaMA: 93% |
| FLAN-T5: 93% |
| BLOOMZ (Arabic): 86% |
| MT0 (Arabic): 84% |
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| You can try our models for auto-grading on Hugging Face! 🌐 |
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| 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. 🌟 |
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| #MachineLearning #AI #Education #EssayGrading #GraduationProject |
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| - **Developed by:** mohamed emam |
| - **Model type:** decoder only |
| - **Language(s) (NLP):** English |
| - **License:** gpl |
| - **Finetuned from model :** Mistral |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** https://github.com/mohamed-em2m/Automatic-Grading-AI |
| - |
| ### Direct Use |
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| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| auto grading for essay quetions |
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| ### Explain how it work |
| - model take three inputs first context or perfect answer + quetion on context + student answer |
| then model output the result |
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|  |
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| ### Training Data |
| - **mohamedemam/Essay-quetions-auto-grading-arabic** |
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|
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| ### Training Procedure |
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|
| 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:" |
| ``` |
|
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|
|
| ## 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 |
|
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| ### 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 |
