---
license: apache-2.0
language:
- en
library_name: transformers
---

# Model Card: bart_fine_tuned_model

<!-- Provide a quick summary of what the model is/does. -->


## Model Name

## generate_summaries

### Model Description

<!-- This model represents a fine-tuned version of the facebook/bart-large model, specifically adapted for the task of Resume Summarization. The model has been trained to efficiently generate concise and relevant summaries from extensive resume texts. The fine-tuning process has tailored the original BART model to specialize in summarization tasks based on a specific dataset.. -->
This model represents a fine-tuned version of the facebook/bart-large model, specifically adapted for the task of Resume Summarization. The model has been trained to efficiently generate concise and relevant summaries from extensive resume texts. 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_summaries**


-**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: 10**
- **Predict with Generate: True**
- **Gradient Accumulation Steps: 1**
- **Optimizer: paged_adamw_32bit**
- **Learning Rate Scheduler Type: cosine**


## how to use

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
**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_summaries'
      tokenizer = BartTokenizer.from_pretrained(model_name)
      model = BartForConditionalGeneration.from_pretrained(model_name)

**4.** Prepare the text for summarization:

      text = 'Your resume text here'
      inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length")

**5.** Generate the summary:

    min_length_threshold = 55
    summary_ids = model.generate(inputs["input_ids"], num_beams=4, min_length=min_length_threshold, max_length=150, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

**6.** Output the summary:

    print("Summary:", summary)

## Model Card Authors

Dereje Hinsermu

## Model Card Contact