Best starting point for NLP projects
December 10, 2022

Best starting point for NLP projects

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Hugging Face

We use Hugging Face models and datasets to design, test a compare multiple approaches for ML projects and, and in general, for research purposes. Thanks to Hugging Face, we do not need extensive training, and our NLP models' fine-tuning is simpler and more cost efficient.

Pros

  • NLP models
  • NLP datasets
  • Version control for models and datasets.

Cons

  • phonetic models
  • phonetic datasets
  • Multiple models for multiple tasks to try
  • Multiple dataset for multiple tasks to try
  • Using Hugging Face is cost efficient vs other paid alternatives
Still need to run more experiments to be able to compare them.

Do you think Hugging Face delivers good value for the price?

Yes

Are you happy with Hugging Face's feature set?

Yes

Did Hugging Face live up to sales and marketing promises?

Yes

Did implementation of Hugging Face go as expected?

Yes

Would you buy Hugging Face again?

Yes

Hugging Face is an excellent starting point when working on NLP projects; it is also great for prototyping and developing pipelines for NLP tasks, being those tasks general like embedding representation or specific, like SQUAD models and datasets. It needs more phonetic models or datasets to be as advantageous in that regard.

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