[Full Picture] Fine-tuning with custom datasets — transformers 3.2.0 documentation

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Fine-tuning with custom datasets — transformers 3.2.0 documentation

Source: huggingface.co

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Summary Analysis Research

Article summary:

1. This tutorial demonstrates how to use 🤗 Transformers models with custom datasets for various downstream tasks.

2. The example of sequence classification with IMDb reviews shows how to download, tokenize, and train a model on the dataset.

3. The article also includes an example of token classification with W-NUT Emerging Entities and provides additional resources for further learning.

Article analysis:

The article titled "Fine-tuning with custom datasets" provides a tutorial on how to use 🤗 Transformers models with custom datasets. The guide covers several examples of using pre-trained models for different downstream tasks such as sequence classification, token classification, and question answering.

One potential bias in the article is that it assumes the reader has prior knowledge of PyTorch/TensorFlow and machine learning concepts. This assumption may exclude beginners who are not familiar with these concepts from understanding the tutorial fully.

The article also promotes the use of 🤗 Transformers models without exploring other alternatives or discussing their limitations. While these models have shown impressive results in various natural language processing tasks, they may not be suitable for all use cases.

Additionally, the article does not discuss potential risks associated with fine-tuning pre-trained models on custom datasets, such as overfitting or bias amplification. It would be helpful to include a section on best practices for mitigating these risks.

Overall, while the tutorial provides useful information on how to fine-tune pre-trained models on custom datasets, it could benefit from more balanced reporting and consideration of alternative approaches and potential risks.

Topics for further research:

Best practices for mitigating overfitting and bias amplification in fine-tuning pre-trained models Limitations of 🤗 Transformers models in natural language processing tasks Alternative approaches to fine-tuning pre-trained models on custom datasets Introduction to PyTorch/TensorFlow and machine learning concepts for beginners Risks associated with using pre-trained models for downstream tasks Comparison of different pre-trained models for natural language processing tasks