# SQuAD Fine-tuning

A question-answering model fine-tuned on the SQuAD dataset based on bert-base-multilingual-cased

This model is a fine-tuned version of DistilBERT on the SQuAD question-answering dataset, designed for QA tasks.

This model is a DistilBERT model fine-tuned on the SQuAD dataset for extractive question answering tasks, with high exact match rate and F1 score.

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on DistilBERT, suitable for reading comprehension tasks.

Lightweight Q&A model based on DistilBERT, fine-tuned on the SQuAD dataset

This model is a question-answering model fine-tuned on the SQuAD dataset based on DistilBERT, designed for reading comprehension tasks.

This is the INT8 quantized version of the DistilBERT base uncased model, specifically designed for question answering tasks, optimized for model size and inference speed through post-training static quantization.

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on DistilBERT, suitable for reading comprehension tasks.

A question-answering model based on DistilBERT, fine-tuned on the SQuAD v2 dataset, suitable for QA tasks

A question-answering model fine-tuned on the squad_v2 dataset based on the distilbert-base-uncased model

This model is a fine-tuned version of the DistilBERT base model on the SQuAD question answering dataset, specifically designed for question answering tasks.

This model is a fine-tuned version of the DistilBERT base model on the SQuAD question answering dataset, designed for question answering tasks.

A question-answering model fine-tuned on the SQuAD 2.0 dataset based on RoBERTa-base, designed to answer questions based on given text

This model is a fine-tuned version of distilbert-base-uncased on the SQuAD question answering dataset, suitable for QA tasks.

A Q&A model fine-tuned on the SQuAD dataset based on the DistilBERT model

This model is a fine-tuned version of DistilBERT on the SQuAD Q&A dataset, suitable for question-answering tasks.

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on the DistilBERT base model, suitable for reading comprehension tasks.

A Q&A model fine-tuned on SQuAD1, AQA, and NewsQA datasets based on the DeBERTa-base model

This model is a fine-tuned version of DeBERTa-base on the SQuAD question answering dataset, designed for automatic question answering tasks

This model is a fine-tuned version of distilbert-base-uncased on the SQuAD adversarial dataset, suitable for question-answering tasks.

A Q&A model based on DistilBERT, fine-tuned on the SQuAD dataset for reading comprehension tasks

This model is a question-answering model fine-tuned on the SQuAD dataset based on DistilBERT, designed for extractive question-answering tasks.

A question-answering model fine-tuned on the SQuAD dataset based on the DistilBERT base model

This model is a question-answering model fine-tuned on the SQuAD dataset based on DistilBERT, suitable for reading comprehension tasks.

This model is a fine-tuned version of DistilBERT on the SQuAD v2 question-answering dataset, designed for Q&A tasks.

This model is a fine-tuned version of the DistilBERT base model on the SQuAD Q&A dataset, suitable for question-answering tasks.

This model is a question-answering model fine-tuned on the SQuAD v2 dataset based on DistilBERT, suitable for reading comprehension tasks.

This model is a question-answering model fine-tuned on the SQuAD 2.0 dataset based on RoBERTa-base, excelling in reading comprehension tasks.

This model is a fine-tuned version of DistilBERT on the SQuAD question answering dataset, designed for QA tasks.

This model is a fine-tuned version of the DistilBERT base model on the SQuAD 2.0 question answering dataset, designed for QA tasks.

A Q&A model fine-tuned on the SQuAD 2.0 dataset based on the RoBERTa-base model

A QA model fine-tuned on SQuAD v1 based on BERT-base uncased, with 66% of linear layer weights pruned via nn_pruning library, achieving 2.32x inference speedup

A question answering system based on the ALBERT-xlarge-v2 model fine-tuned on the SQuAD V2 dataset

A Polish Q&A system fine-tuned on the multilingual BERT model, performing excellently on the Polish SQuAD1.1 dataset

This model is a fine-tuned version of the DistilBERT base model on the SQuAD question answering dataset, designed for question answering tasks.

XLNet is a pre-trained language model jointly developed by Google and Carnegie Mellon University, fine-tuned on the SQuAD 2.0 question answering dataset

This model is a question-answering model fine-tuned on the SQuAD dataset based on distilbert-base-cased, specifically trained for the Keras.io question-answering tutorial.

This model is a lightweight version based on DistilBERT, fine-tuned on the SQuAD question-answering dataset for Q&A tasks.

A question-answering model fine-tuned and optimized on SQuAD v1 based on BERT-base uncased, retaining 43% of original weights through pruning techniques, achieving 1.96x faster inference speed

Lightweight QA model based on DistilBERT, fine-tuned on the SQuAD dataset