Model Card: generate_reason

Model Name

generate_reason

Model Description

This model represents a fine-tuned version of the facebook/bart-large model, specifically adapted for the task of reason generator by annalysing resume with job description. The model has been trained to efficiently generate concise and relevant reason from extensive resume texts and JD. The fine-tuning process has tailored the original BART model to specialize in summarization tasks based on a specific dataset.

Model information

-Base Model: GebeyaTalent/generate_reason

-Finetuning Dataset: To be made available in the future.

Training Parameters

Evaluation Strategy: epoch:
Learning Rate: 5e-5
Per Device Train Batch Size: 8:
Per Device Eval Batch Size: 8
Weight Decay: 0.01
Save Total Limit: 5
Number of Training Epochs: 5
Predict with Generate: True
Gradient Accumulation Steps: 1
Optimizer: paged_adamw_32bit
Learning Rate Scheduler Type: cosine

how to use

1. Install the transformers library:

pip install transformers

2. Import the necessary modules:

  import torch
  from transformers import BartTokenizer, BartForConditionalGeneration

3. Initialize the model and tokenizer:

  model_name = 'GebeyaTalent/generate_reason'
  tokenizer = BartTokenizer.from_pretrained(model_name)
  model = BartForConditionalGeneration.from_pretrained(model_name)

4. Prepare the text to generate reason:

resume = 'your resume text here"
job_description = "your job_description here"

Concatenate the resume and job description with a delimiter

combined_text = "Resume: " + ["resume"] + " Job Description: " + ["job_description"]


inputs = tokenizer(combined_text, return_tensors="pt", truncation=True, padding="max_length", max_length=1024)

5. Generate reason

reason_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=150, early_stopping=True)
reason = tokenizer.decode(reason_ids[0], skip_special_tokens=True)

6. Output the summary:

print("Reason:", reason)

Model Card Authors

Dereje Hinsermu

GebeyaTalent
/

generate_reason