Generate Cover Letter

README.md

@@ -0,0 +1,56 @@
+---
+datasets:
+- ShashiVish/cover-letter-dataset
+language:
+- en
+---
+
+
+### Generate Cover Letter
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_name = "ShashiVish/llama-7b-merged-int4-r512-cover-letter"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+model = model.to('cuda')
+
+job_title = "Senior Java Developer"
+preferred_qualification = "3+ years of Java, Spring Boot"
+hiring_company_name = "Google"
+user_name = "Emily Evans"
+past_working_experience= "Java Developer at XYZ for 4 years"
+current_working_experience = "Senior Java Developer at ABC for 1 year"
+skilleset= "Java, Spring Boot, Microservices, SQL, AWS"
+qualification = "Master's in Electronics Science"
+
+item = {'job_title': "Senior Java Developer", 'preferred_qualification': "5+ years of Java, Spring Boot",
+            'hiring_company_name': "Netflix", 'user_name': "Emily Evans",
+            'past_working_experience': "Java Developer at XYZ for 4 years",
+            'current_working_experience': "Senior Java Developer at ABC for 1 year",
+            'skilleset': "Java, Spring Boot, Microservices, SQL, AWS",
+            'qualification': "Master's in Computer Science"}
+
+prompt = f"""### Instruction:
+You are a smart cover letter generator. Use following Input to generate Cover letter.
+
+### Input:
+Role: item['job_title'], Preferred Qualifications: {item['preferred_qualification']}, \
+                        Hiring Company: {item['hiring_company_name']}, User Name: {item['user_name']}, \
+                        Past Working Experience: {item['past_working_experience']}, \
+                        Current Working Experience: {item['current_working_experience']}, \
+                        Skillsets: {item['skilleset']}, Qualifications: {item['qualification']}
+
+### Cover Letter:
+"""
+
+input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
+outputs = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, top_p=0.9,temperature=0.9)
+#model_response = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]
+model_response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0][len(prompt):]
+
+print(model_response)
+
+```