## Albert Transformer on SQuAD-v2 Training is done on the [SQuAD_v2](https://rajpurkar.github.io/SQuAD-explorer/) dataset. The model can be accessed via HuggingFace: ## Model Specifications We have used the following parameters: - num_train_epochs=0.25, - per_device_train_batch_size=5, - per_device_eval_batch_size=10, - warmup_steps=100, - weight_decay=0.01, ## Usage Specifications ```python from transformers import AutoTokenizer,AutoModelForQuestionAnswering from transformers import pipeline model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/albert-squad-v2') tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-squad-v2') nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer) QA_inp={ 'question': 'How many parameters does Bert large have?', 'context': 'Bert large is really big... it has 24 layers, for a total of 340M parameters.Altogether it is 1.34 GB so expect it to take a couple minutes to download to your Colab instance.' } result=nlp_QA(QA_inp) result ``` ## Result The result is: {'answer': '340M', 'end': 65, 'score': 0.14847151935100555, 'start': 61} --- language: - en license: apache-2.0 datasets: - squad_v2 model-index: - name: abhilash1910/albert-squad-v2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - name: Exact Match type: exact_match value: 23.6563 verified: true - name: F1 type: f1 value: 29.3808 verified: true ---