Updated Model Usage

README.md

@@ -5,4 +5,89 @@ language:
 - en
 metrics:
 - bleu
----
+tags:
+  - text2text-generation
+---
+# Usage
+
+Plese find below example how to generate cover letter for input.
+
+### Running the model on a GPU
+
+
+```python
+
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained("ShashiVish/t5-base-fine-tune-1024-cover-letter")
+model = T5ForConditionalGeneration.from_pretrained("ShashiVish/t5-base-fine-tune-1024-cover-letter" , max_length = 512 , device_map="auto")
+
+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"
+
+
+input_text = f" Generate Cover Letter for Role: {job_title}, \
+ Preferred Qualifications: {preferred_qualification}, \
+ Hiring Company: {hiring_company_name}, User Name: {user_name}, \
+ Past Working Experience: {past_working_experience}, Current Working Experience: {current_working_experience}, \
+ Skillsets: {skilleset}, Qualifications: {qualification} "
+
+# Tokenize and generate predictions
+input_ids = tokenizer.encode(input_text, return_tensors='pt', max_length=2048, truncation=False, padding=True)
+input_ids = input_ids.to('cuda')
+output_ids = model.generate(input_ids)
+
+# Decode the output
+output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+print("Generated Cover Letter:")
+print(output_text)
+
+```
+
+
+### Running the model on a CPU
+
+
+```python
+
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained("ShashiVish/t5-base-fine-tune-1024-cover-letter")
+model = T5ForConditionalGeneration.from_pretrained("ShashiVish/t5-base-fine-tune-1024-cover-letter" , max_length = 512 )
+
+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"
+
+
+input_text = f" Generate Cover Letter for Role: {job_title}, \
+ Preferred Qualifications: {preferred_qualification}, \
+ Hiring Company: {hiring_company_name}, User Name: {user_name}, \
+ Past Working Experience: {past_working_experience}, Current Working Experience: {current_working_experience}, \
+ Skillsets: {skilleset}, Qualifications: {qualification} "
+
+# Tokenize and generate predictions
+input_ids = tokenizer.encode(input_text, return_tensors='pt', max_length=2048, truncation=False, padding=True)
+output_ids = model.generate(input_ids)
+
+# Decode the output
+output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+print("Generated Cover Letter:")
+print(output_text)
+
+```
+
+