Pierreguillou

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Average Model Cost: $0.0000

Number of Runs: 28,011

Models by this creator

bert-large-cased-squad-v1.1-portuguese

The bert-large-cased-squad-v1.1-portuguese model is a Portuguese BERT language model that has been fine-tuned on the SQUAD v1.1 dataset for question-answering tasks. It uses the BERTimbau Large model, which is a pretrained BERT model for Brazilian Portuguese. The model achieves state-of-the-art performance on tasks such as Named Entity Recognition, Sentence Textual Similarity, and Recognizing Textual Entailment. The model can be used with the Pipeline or Auto classes and is available for cloning. However, it is important to note that the training data may contain biases and unfiltered content. The model was trained and evaluated by Pierre GUILLOU with the help of various organizations and platforms, including Hugging Face, Neuralmind.ai, Deep Learning Brasil group, and AI Lab. If you use the model, please cite the relevant sources.

$-/run

8.9K

Huggingface

bert-base-cased-squad-v1.1-portuguese

pierreguillou

The bert-base-cased-squad-v1.1-portuguese model is a Portuguese question answering model. It is based on the BERT language model and was fine-tuned on the SQUAD v1.1 dataset in Portuguese. The model achieves state-of-the-art performance on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity, and Recognizing Textual Entailment. It can be used with the Pipelin and Auto classes in the Hugging Face library. However, it's important to note that the training data used for this model may contain biases and unfiltered content. The model was developed by Pierre GUILLOU in collaboration with various organizations and platforms.

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5.5K

Huggingface

layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512

pierreguillou

The layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512 model is a token classification model that has been fine-tuned to perform layout analysis on documents at the paragraph level. It is based on the layout-xlm-base model and has been further trained with the DocLayNet dataset. The model can be used to identify and classify different layout elements in documents, such as headings, paragraphs, lists, and tables, by predicting the token-level labels for each input token.

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5.0K

Huggingface

lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384

pierreguillou

Document Understanding model (finetuned LiLT base at line level on DocLayNet base) This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base with the DocLayNet base dataset. It achieves the following results on the evaluation set: Loss: 1.0003 Precision: 0.8584 Recall: 0.8584 F1: 0.8584 Tokens Accuracy: 0.8584 Line Accuracy: 0.9197 Accuracy at line level Line Accuracy: 91.97% Accuracy by label Caption: 79.42% Footnote: 68.21% Formula: 98.02% List-item: 82.72% Page-footer: 99.17% Page-header: 84.18% Picture: 83.2% Section-header: 76.92% Table: 97.65% Text: 91.17% Title: 77.46% References Blog posts Layout XLM base (03/05/2023) Document AI | Inference APP and fine-tuning notebook for Document Understanding at line level with LayoutXLM base LiLT base (02/16/2023) Document AI | Inference APP and fine-tuning notebook for Document Understanding at paragraph level (02/14/2023) Document AI | Inference APP for Document Understanding at line level (02/10/2023) Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset (01/31/2023) Document AI | DocLayNet image viewer APP (01/27/2023) Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference) Notebooks (paragraph level) LiLT base Document AI | Inference APP at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset) Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset) Document AI | Fine-tune LiLT on DocLayNet base in any language at paragraph level (chunk of 512 tokens with overlap) Notebooks (line level) Layout XLM base Document AI | Inference at line level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet dataset) Document AI | Inference APP at line level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet base dataset) Document AI | Fine-tune LayoutXLM base on DocLayNet base in any language at line level (chunk of 384 tokens with overlap) LiLT base Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset) Document AI | Inference APP at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset) Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap) DocLayNet image viewer APP Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference) APP You can test this model with this APP in Hugging Face Spaces: Inference APP for Document Understanding at line level (v1). DocLayNet dataset DocLayNet dataset (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets: direct links: doclaynet_core.zip (28 GiB), doclaynet_extra.zip (7.5 GiB) Hugging Face dataset library: dataset DocLayNet Paper: DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis (06/02/2022) Model description The model was finetuned at line level on chunk of 384 tokens with overlap of 128 tokens. Thus, the model was trained with all layout and text data of all pages of the dataset. At inference time, a calculation of best probabilities give the label to each line bounding boxes. Inference See notebook: Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset) Training and evaluation data See notebook: Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap) Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 5e-05 train_batch_size: 8 eval_batch_size: 16 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 5 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.26.0 Pytorch 1.13.1+cu116 Datasets 2.9.0 Tokenizers 0.13.2 Other models Line level Document Understanding model (finetuned LiLT base at line level on DocLayNet base) (accuracy | tokens: 85.84% - lines: 91.97%) Document Understanding model (finetuned LayoutXLM base at line level on DocLayNet base) (accuracy | tokens: 93.73% - lines: ...) Paragraph level Document Understanding model (finetuned LiLT base at paragraph level on DocLayNet base) (accuracy | tokens: 86.34% - paragraphs: 68.15%) Document Understanding model (finetuned LayoutXLM base at paragraph level on DocLayNet base) (accuracy | tokens: 96.93% - paragraphs: 86.55%)

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3.9K

Huggingface

gpt2-small-portuguese

pierreguillou