Msroberta

🚀 MSRoBERTa

A fine-tuned RoBERTa MLM model for the Miscrosoft Sentence Completion Challenge. This model is case-sensitive, following the Roberta-base model.

🚀 Quick Start

This is a fine - tuned RoBERTa MLM model specifically designed for the Miscrosoft Sentence Completion Challenge. It inherits the case - sensitivity from the Roberta - base model.

📚 Documentation

Model description (taken from: here)

RoBERTa is a transformers model pretrained on a large corpus of English data in a self - supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.

More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.

💻 Usage Examples

Basic Usage

You can use this model directly with a pipeline for masked language modeling:

from transformers import pipeline,AutoModelForMaskedLM,AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nkoh01/MSRoberta")
model = AutoModelForMaskedLM.from_pretrained("nkoh01/MSRoberta")

unmasker = pipeline(
    "fill-mask",
    model=model,
    tokenizer=tokenizer
)
unmasker("Hello, it is a <mask> to meet you.")

[{'score': 0.9508683085441589,
  'sequence': 'hello, it is a pleasure to meet you.',
  'token': 10483,
  'token_str': ' pleasure'},
 {'score': 0.015089659951627254,
  'sequence': 'hello, it is a privilege to meet you.',
  'token': 9951,
  'token_str': ' privilege'},
 {'score': 0.013942377641797066,
  'sequence': 'hello, it is a joy to meet you.',
  'token': 5823,
  'token_str': ' joy'},
 {'score': 0.006964420434087515,
  'sequence': 'hello, it is a delight to meet you.',
  'token': 13213,
  'token_str': ' delight'},
 {'score': 0.0024567877408117056,
  'sequence': 'hello, it is a honour to meet you.',
  'token': 6671,
  'token_str': ' honour'}]

📦 Installation

Make sure you run !pip install transformers command to install the transformers library before running the commands above.

🔧 Technical Details

Bias and limitations

Under construction.