Roberta Base Best Squad2

🚀 RoBERTa-base for QA

This project utilizes the 'roberta-base' language model for extractive question - answering tasks, trained and evaluated on the SQuAD 2.0 dataset.

🚀 Quick Start

The model can be easily used for question - answering tasks. You can refer to the usage examples below to get started.

✨ Features

• Language Model: 'roberta - base'
• Language: English
• Downstream - task: Extractive QA
• Training data: SQuAD 2.0
• Eval data: SQuAD 2.0

📦 Installation

The README doesn't provide specific installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "PremalMatalia/roberta-base-best-squad2"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Which name is also used to describe the Amazon rainforest in English?',
    'context': 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet\'s remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.'
}
res = nlp(QA_input)
print(res)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Advanced Usage

There is no advanced usage code provided in the original README, so this part is skipped.

📚 Documentation

Env Information

transformers version: 4.9.1 Platform: Linux - 5.4.104 + - x86_64 - with - Ubuntu - 18.04 - bionic Python version: 3.7.11 PyTorch version (GPU?): 1.9.0+cu102 (False) Tensorflow version (GPU?): 2.5.0 (False)

Hyperparameters

max_seq_len=386
doc_stride=128
n_best_size=20
max_answer_length=30
min_null_score=7.0
batch_size=8

n_epochs=6
base_LM_model = "roberta-base"
learning_rate=1.5e-5
adam_epsilon=1e-5
adam_beta1=0.95
adam_beta2=0.999
warmup_steps=100
weight_decay=0.01
optimizer=AdamW
lr_scheduler="polynomial"

⚠️ Important Note

There is a special threshold value CLS_threshold=-3 used to more accurately identify no answers [Logic will be available in GitHub Repo [TBD]

Performance

"exact": 81.192622
"f1":    83.95408
"total": 11873
"HasAns_exact": 74.190283
"HasAns_f1":    79.721119
"HasAns_total": 5928
"NoAns_exact":  88.174937
"NoAns_f1":     88.174937
"NoAns_total":  5945

🔧 Technical Details

The README doesn't provide detailed technical implementation information, so this section is skipped.

📄 License

The README doesn't provide license information, so this section is skipped.

Authors

Premal Matalia