GebeyaTalent/generate_summaries

Model Card: bartfinetuned_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:

modelname = 'GebeyaTalent/generatesummaries' tokenizer = BartTokenizer.frompretrained(modelname) model = BartForConditionalGeneration.frompretrained(modelname)

4. Prepare the text for summarization:

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

5. Generate the summary:

minlengththreshold = 55 summaryids = model.generate(inputs["inputids"], numbeams=4, minlength=minlengththreshold, maxlength=150, earlystopping=True) summary = tokenizer.decode(summaryids[0], skipspecial_tokens=True)

6. Output the summary:

print("Summary:", summary)

Model Card Authors

Dereje Hinsermu

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