metadata
language: en
tags:
- question-answering
- squad
- transformers
datasets:
- squad
metrics:
- exact_match
- f1
model-index:
- name: HariomSahu/albert-squad-qa
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: SQuAD
type: squad
metrics:
- type: exact_match
value: N/A
- type: f1
value: 89.93540108105752
albert-base-v2 fine-tuned on SQuAD
This model is a fine-tuned version of albert-base-v2 on the SQuAD dataset.
Training Details
Training Hyperparameters
- Model: albert-base-v2
- Dataset: SQuAD
- Optimizer: adamw
- Learning Rate Scheduler: cosine_with_restarts
- Learning Rate: 6e-05
- Batch Size: 28 per device
- Total Batch Size: 224
- Epochs: 6 (with early stopping)
- Weight Decay: 0.005
- Warmup Ratio: 0.08
- Max Gradient Norm: 0.5
Early Stopping
- Patience: 4
- Metric: f1
- Best Epoch: 2
Usage
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("HariomSahu/albert-squad-qa")
model = AutoModelForQuestionAnswering.from_pretrained("HariomSahu/albert-squad-qa")
# Example usage
question = "What is the capital of France?"
context = "France is a country in Europe. Its capital city is Paris."
inputs = tokenizer(question, context, return_tensors="pt")
outputs = model(**inputs)
# Get answer
start_scores, end_scores = outputs.start_logits, outputs.end_logits
start_index = start_scores.argmax()
end_index = end_scores.argmax()
answer = tokenizer.decode(inputs["input_ids"][0][start_index:end_index+1])
print(f"Answer: {answer}")
Evaluation Results
The model achieved the following results on the evaluation set:
- Exact Match: 80.4541
- F1 Score: 88.7676
Training Configuration Hash
Config Hash: a8d23824
This hash can be used to reproduce the exact training configuration.