模型:
rubentito/longformer-base-mpdocvqa
这是基于SQuAD v1数据集训练的Longformer-base模型,在Multipage DocVQA(MP-DocVQA)数据集上进行了微调。
该模型在 Hierarchical multimodal transformers for Multi-Page DocVQA 中被用作基准。
如何使用此模型在PyTorch中对样本问题和上下文进行推理:
from transformers import LongformerTokenizerFast, LongformerForQuestionAnswering tokenizer = LongformerTokenizerFast.from_pretrained("rubentito/longformer-base-mpdocvqa") model = LongformerForQuestionAnswering.from_pretrained("rubentito/longformer-base-mpdocvqa") text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this." question = "What has Huggingface done?" encoding = tokenizer(question, text, return_tensors="pt") output = model(encoding["input_ids"], attention_mask=encoding["attention_mask"]) start_pos = torch.argmax(output.start_logits, dim=-1).item() end_pos = torch.argmax(output.end_logits, dim=-1).item() context_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist()) answer_tokens = context_tokens[start_pos: end_pos + 1] pred_answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
平均归一化Levenshtein相似度(ANLS)
这是文本型VQA任务(ST-VQA和DocVQA)的标准度量指标。它评估了方法的推理能力,同时对OCR识别错误进行平滑惩罚。详细信息请参见 Scene Text Visual Question Answering 。
答案页预测准确率(APPA)
在MP-DocVQA任务中,模型可以提供包含回答问题所需信息的页码索引。对于这个子任务,使用准确率来评估预测的正确性:即预测的页码是否正确。详细信息请参见 Hierarchical multimodal transformers for Multi-Page DocVQA 。
在 Hierarchical multimodal transformers for Multi-Page DocVQA 的表2中可以找到扩展的实验结果。您还可以在 RRC Portal 上查看实时排行榜。
Model | HF name | Parameters | ANLS | APPA |
---|---|---|---|---|
1238321 | rubentito/bert-large-mpdocvqa | 334M | 0.4183 | 51.6177 |
1239321 | rubentito/longformer-base-mpdocvqa | 148M | 0.5287 | 71.1696 |
12310321 | rubentito/bigbird-base-itc-mpdocvqa | 131M | 0.4929 | 67.5433 |
12311321 | rubentito/layoutlmv3-base-mpdocvqa | 125M | 0.4538 | 51.9426 |
12312321 | rubentito/t5-base-mpdocvqa | 223M | 0.5050 | 0.0000 |
12313321 | rubentito/hivt5-base-mpdocvqa | 316M | 0.6201 | 79.23 |
@article{tito2022hierarchical, title={Hierarchical multimodal transformers for Multi-Page DocVQA}, author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest}, journal={arXiv preprint arXiv:2212.05935}, year={2022} }