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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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--- |
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# Model Card: bart_fine_tuned_model |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Name |
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## generate_summaries |
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### Model Description |
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<!-- 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.. --> |
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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. |
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### Model information |
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-**Base Model: GebeyaTalent/generate_summaries** |
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-**Finetuning Dataset: To be made available in the future.** |
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### Training Parameters |
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- **Evaluation Strategy: epoch:** |
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- **Learning Rate: 5e-5** |
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- **Per Device Train Batch Size: 8:** |
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- **Per Device Eval Batch Size: 8** |
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- **Weight Decay: 0.01** |
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- **Save Total Limit: 5** |
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- **Number of Training Epochs: 10** |
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- **Predict with Generate: True** |
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- **Gradient Accumulation Steps: 1** |
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- **Optimizer: paged_adamw_32bit** |
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- **Learning Rate Scheduler Type: cosine** |
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## how to use |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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**1.** Install the transformers library: |
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**pip install transformers** |
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**2.** Import the necessary modules: |
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import torch |
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from transformers import BartTokenizer, BartForConditionalGeneration |
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**3.** Initialize the model and tokenizer: |
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model_name = 'GebeyaTalent/generate_summaries' |
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tokenizer = BartTokenizer.from_pretrained(model_name) |
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model = BartForConditionalGeneration.from_pretrained(model_name) |
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**4.** Prepare the text for summarization: |
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text = 'Your resume text here' |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length") |
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**5.** Generate the summary: |
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min_length_threshold = 55 |
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summary_ids = model.generate(inputs["input_ids"], num_beams=4, min_length=min_length_threshold, max_length=150, early_stopping=True) |
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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**6.** Output the summary: |
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print("Summary:", summary) |
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## Model Card Authors |
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Dereje Hinsermu |
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## Model Card Contact |
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