Transfer Learning for Text Classification Using Hugging Face Transformers Trainer | Deep Learning
Hugging Face provides three ways to fine-tune a pretrained text classification model: Tensorflow Keras, PyTorch, and transformer trainer. Transformer trainer is an API for feature-complete training in PyTorch without writing all the loops. This tutorial will use the transformer trainer to fine-tune a text classification model. We will talk about the following:
👉 How does transfer learning work?
👉 How to convert a pandas dataframe into a Hugging Face Dataset?
👉 How to tokenize text, load a pretrained model, set training arguments, and train a transfer learning model?
👉 How to make predictions and evaluate the model performance of a fine-tuned transfer learning model for text classification?
👉 How to save the model and re-load the model?
⏰ Timecodes ⏰
0:00 - Intro
00:52 - Step 0: Transfer Learning Algorithms
1:52 - Step 1: Install and Import Libraries
2:17 - Step 2: Download And Read Data
3:57 - Step 3: Train Test Split
4:25 - Step 4: Convert Pandas Dataframe to Hugging Face Dataset
5:41 - Step 5: Tokenize Text
9:13 - Step 6: Load Pretrained Model
9:45 - Step 7: Set Training Argument
11:42 - Step 8: Set Evaluation Metrics
12:10 - Step 9: Train Model Using Transformer Trainer
13:04 - Step 10: Make Predictions for Text Classification
14:40 - Step 11: Model Performance Evaluation
15:06 - Step 12: Save and Load Model
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📺 Videos mentioned in the video
Google Colab Tutorial for Beginners: • Google Colab Tutorial For Beginners
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