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Hugging Face, a great library for using transformers
What do you like best about the product?
Hugging Face is the most complete library for using transformers-based neuronal networks. The library contains several pre-trained models, as well as already fine-tuned models. Besides, its website contains a tutorial quite useful for learning how to use the library.
You can search for different models by using the models option in the website. There, you can select models by language, task, etc. One advantage is that you can test the model in the website using a textbox.
You can search for different models by using the models option in the website. There, you can select models by language, task, etc. One advantage is that you can test the model in the website using a textbox.
What do you dislike about the product?
It is difficult to find something bad in this library. Maybe, I could say that sometimes it is difficult to find a specific model using the search option in the website and it is better to google for the model.
Sometimes, I find quite difficult to find a more detailed description of some features of the library. anyway, there is a lot of information, and tutorials, available in other websites.
Sometimes, I find quite difficult to find a more detailed description of some features of the library. anyway, there is a lot of information, and tutorials, available in other websites.
What problems is the product solving and how is that benefiting you?
Hugging Face is oriented to text-processing using transformers-based neuronal networks. By using the Hugging Face library, you can use a high level API for doing text classification, question answering, etc. For me, it is quite easy to use it.
Currently, the website contains most of the best systems for doing natural language processing. Giving the huge amount of models available, you can select the most suitable for your task. It is very easy to switch from one model to another one. You only have to change the name of the model in your code.
Currently, the website contains most of the best systems for doing natural language processing. Giving the huge amount of models available, you can select the most suitable for your task. It is very easy to switch from one model to another one. You only have to change the name of the model in your code.
Recommendations to others considering the product:
My first advice is to complete the tutorial and try all the examples. Then, I recommend to modify the examples testing new features.
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