Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+---
+
+# Model Card: bart_fine_tuned_model-v2
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+## Model Name
+
+## bart_fine_tuned_model-v2
+
+### 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: derekiya/bart_fine_tuned_model-v2**
+
+
+-**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 = 'derekiya/bart_fine_tuned_model-v2'
+      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
+