Hugging Face

Hugging Face supports around 100,000 pre-trained language models that can be used for various NLP tasks. The Hugging Face transformers library, which is a popular choice for NLP tasks such as text classification and machine translation, currently supports over 100 pre-trained language models. These models include popular models such as BERT, GPT-2, and RoBERTa. In addition Hugging Face provides tools and libraries that allow users to fine-tune and customize these models for specific tasks or datasets.

A Hugging Face Course – https://github.com/huggingface/course

Hugging Face on AWS blog – https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/

CEO Clement Delangue, calls it the “GitHub of machine learning.” Its emphasis on an open, collaborative approach that made investors confident in the company’s $2 billion valuation, he said. “That’s what is really important to us, makes us successful and makes us different from others in the space.” 

DistilBERT is a smaller, faster, and cheaper version of the BERT language model developed by Hugging Face by controlling the loss function during training of a ‘student model’ from a ‘teacher model’. It bucks the trend towards larger models, and instead focusses on training a more efficient model. It has been “distilled” to reduce its size and computational requirements, making it faster to train and more efficient to run. Despite being smaller than BERT, DistilBERT is able to achieve similar or even slightly better performance on many NLP tasks. The triple loss function is devised to include a distillation loss, a training loss and a cosine-distance loss.

Examples of generative models available on the Hugging Face platform include:

  1. GPT-2: GPT-2 (Generative Pre-training Transformer 2) is a large-scale language model developed by OpenAI that can be used for tasks such as language translation and text generation.
  2. BERT: BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that can be used for tasks such as language translation and text classification.
  3. RoBERTa: RoBERTa (Robustly Optimized BERT Approach) is a language model developed by Facebook that is based on the BERT model and can be used for tasks such as language translation and text classification.
  4. T5: T5 (Text-To-Text Transfer Transformer) is a language model developed by Google that can be used for tasks such as language translation and text summarization.
  5. DistilBERT, described above. To generate text with DistilBERT, you would typically fine-tune the model on a specific task, such as machine translation or language generation, using a dataset that is relevant to the task. Once the model has been fine-tuned, you can use it to generate text by providing it with a prompt or seed text and letting it predict the next word or sequence of words.

Docs on text generation – https://huggingface.co/transformers/v3.1.0/main_classes/model.html?highlight=generate

Here’s an example of using transformers to generate some text.

import transformers

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('distilgpt2') 
model = AutoModelWithLMHead.from_pretrained('distilgpt2')  

# Encode the prompt
input_context_prompt = "Men on the moon "
input_ids = tokenizer.encode(input_context_prompt, return_tensors='pt')  # encode input context

# Generate text
outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.9, num_return_sequences=10, do_sample=True)  

# Sample candidate outputs and print
for i in range(10): #  10 output sequences were generated
    print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

Note the temperature parameter during model.generate(). A temperature of zero means the generation process will choose the most likely next word . A higher temperature allows for less likely words to be included in the generation process.

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