run_fill_mask.txt

demonstrating the Fill-Mask task (also known as Masked Language Modeling).

This task involves predicting a word that has been hidden (masked) in a sentence. It's fundamental to how models like BERT and RoBERTa are pre-trained.

Prerequisites:

Ensure you have the necessary libraries. RoBERTa uses sentencepiece.

Bash

pip install transformers sentencepiece torch
# Or: pip install transformers sentencepiece tensorflow
(Ensure your existing virtual environment is active).

Explicit Model Choice:

We will use roberta-base. This uses the RoBERTa architecture, which has some improvements over the original BERT, and we haven't explicitly used a RoBERTa model in previous examples. It's important to note RoBERTa uses <mask> as its mask token, unlike BERT which uses [MASK].

How to Run:

Make sure you've run pip install transformers sentencepiece torch (or the tensorflow equivalent) in your activated virtual environment.
Save the code above into a file named run_fill_mask.py.
Open your Ubuntu terminal.
Make sure your virtual environment is activated (source .venv/bin/activate).
Run the script:
Bash

python run_fill_mask.py
What to Expect:

First Run: It will download the roberta-base model files (similar size to BERT-base, around 500MB) and cache them locally.
Prediction Execution: The model will analyze the context around the <mask> token in the input sentence.
Output: It will print the top 5 most likely words (according to the RoBERTa model) to fill the mask. For the example sentence, you should expect predictions related to the end of the work week, such as:
weekend
break
evening
end
start (as in, start of the weekend) The output will show the score for each prediction and the complete sentence with the mask filled by that prediction.
This example demonstrates how to use a pre-trained model like RoBERTa for its fundamental task of predicting masked words, showcasing its understanding of language context, all running locally.