NLTK VS bert

Using the NTLK Natural Language Toolkit

Is this just using LLM to be cool? How does pure LLM with simple "In the scale between 0-10"" stack up against traditional, battle-tested sentiment analysis tools?

Gemini suggests NLTK and spaCy

https://www.nltk.org/

https://spacy.io/

alternatively, could we not simply split by common characters such as newlines and periods, to split it within sentences? it would be fragile with special handling required for numbers with decimal points and probably various other edge cases, though.

there are also Python libraries meant for natural language parsing[0] that could do that task for us. I even see examples on stack overflow[1] that simply split text into sentences.

[0]: https://www.nltk.org/

Check out https://www.nltk.org/ and work through it, it'll give you a foundational understanding of how all this works, but very basically it's just a fancy auto-complete.

NLTK Documentation

If this is True, then you need also Natural Language Toolkit to process the words.

In that case, you should definitely have a look at Python's nltk library which stands for Natural Language Toolkit. They have a rich corpus collection for all kinds of specialized things like grammars, taggers, chunkers, etc.

BERT

In 2019, a new language representation called BERT (Bedirectional Encoder Representation from Transformers) was introduced. The main idea behind this paradigm is to first pre-train a language model using a massive amount of unlabeled data then fine-tune all the parameters using labeled data from the downstream tasks. This allows the model to generalize well to different NLP tasks. Moreover, it has been shown that this language representation model can be used to solve downstream tasks without being explicitly trained on, e.g classify a text without training phase.

task-specific parameters, and is trained on the downstream tasks by simply fine-tuning all pre-trained parameters.

[0] https://arxiv.org/abs/1810.04805

The release of BERT in 2018 kicked off the language model revolution. The Transformers architecture succeeded RNNs and LSTMs to become the architecture of choice. Unbelievable progress was made in a number of areas: summarization, translation, text classification, entity classification and more. 2023 tooks things to another level with the rise of large language models (LLMs). Models with billions of parameters showed an amazing ability to generate coherent dialogue.

The general idea is that you have a particular task & dataset, and you optimize these vectors to maximize that task. So the properties of these vectors - what information is retained and what is left out during the 'compression' - are effectively determined by that task.

In general, the core task for the various "LLM tools" involves prediction of a hidden word, trained on very large quantities of real text - thus also mirroring whatever structure (linguistic, syntactic, semantic, factual, social bias, etc) exists there.

If you want to see how the sausage is made and look at the actual algorithms, then the key two approaches to read up on would probably be Mikolov's word2vec (https://arxiv.org/abs/1301.3781) with the CBOW (Continuous Bag of Words) and Continuous Skip-Gram Model, which are based on relatively simple math optimization, and then on the BERT (https://arxiv.org/abs/1810.04805) structure which does a conceptually similar thing but with a large neural network that can learn more from the same data. For both of them, you can either read the original papers or look up blog posts or videos that explain them, different people have different preferences on how readable academic papers are.

Could you post a link to "the BERT paper"? I've read some, but would be interested reading anything that anyone considered definitive :) Is it this one? "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" :https://arxiv.org/abs/1810.04805

Rust crate rust_bert implementation of the BERT language model (https://arxiv.org/abs/1810.04805 Devlin, Chang, Lee, Toutanova, 2018). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:

The achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run, the paper [1] mentions that the pretraining took 4 days on 16 TPU chips for the BERT-Base model and 4 days on 64 TPU chips for the BERT-Large model.

Regarding the translation of these techniques to the pretraining phase for a GPT model, it is possible that some of the optimizations and techniques used for BERT could be applied to GPT as well. However, the specific architecture and training objectives of GPT might require different approaches or additional optimizations.

As for the SOPHIA optimizer, it is designed to improve the training of deep learning models by adaptively adjusting the learning rate and momentum. According to the paper [2], SOPHIA has shown promising results in various deep learning tasks. It is possible that the SOPHIA optimizer could help improve the training of BERT and GPT models, but further research and experimentation would be needed to confirm its effectiveness in these specific cases.

[1] https://arxiv.org/abs/1810.04805

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