What is the process of Fine tuning a model?

Hello, I would like to create a developer tool to track/monitor hate speech detection on Twitter. I would like to create a classification model that can detect hate speech in tweets using the tweet-eval dataset, but after completing the third chapter of the course I’m having trouble. Can someone lead me to resources clarifying how I can fine-tune a speech detection model using the “bert-base-uncased” as my checkpoint?

hi @BinaryCoffee .

i think you can use BertForSequenceClassification finetuning to ust hate speech detection.

the process of fine-tuning is

  1. preprocessing custom dataset for fine tuning
  2. build data loader to load custom dataset
  3. build fine-tuning model (hugging-face support many library of fine_tuning)
  4. load pre-trained model and fine-tuning
  5. metric performance fine-tuned model.

there are some helpful link to you.

hugging face docs of BertForSequenceClassification.

[BertForSequenceClassification docs]

thank to HF team and @nielsr !
they support various fine-tuning tutorial of various models with colab.
[huggingface transformer tutorial page]

hope to help.

regards.

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