Update README.md

README.md

@@ -1,30 +1,190 @@
 ---
 language:
-- ar
 - en
+- ar
 license: gpl
 tags:
 - autograding
-- 'essay quetion '
+- essay quetion
 - sentence similarity
 metrics:
 - accuracy
 library_name: peft
+datasets:
+- mohamedemam/Essay-quetions-auto-grading
 ---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### 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. 📝✨
 
 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%
+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
+#MachineLearning #AI #Education #EssayGrading #GraduationProject
+
+- **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
+### 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]
+
+<!-- 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 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
+
+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")
+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