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---
language: multilingual
tags:
- document-classification
- text-classification
- multilingual
- doclaynet
- e5
pipeline_tag: text-classification
base_model: intfloat/multilingual-e5-large
datasets:
- pierreguillou/DocLayNet-base
metrics:
- accuracy
model-index:
- name: multilingual-e5-doclaynet
results:
- task:
type: text-classification
name: Document Classification
dataset:
name: DocLayNet
type: pierreguillou/DocLayNet-base
metrics:
- type: accuracy
value: 0.9719
name: Test Accuracy
- type: loss
value: 0.5192
name: Test Loss
library_name: transformers
---
# Multilingual E5 for Document Classification (DocLayNet)
This model is a fine-tuned version of intfloat/multilingual-e5-large for document text classification based on the DocLayNet dataset.
## Evaluation results
- Test Loss: 0.5192, Test Acc: 0.9719
## Usage:
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="kaixkhazaki/multilingual-e5-doclaynet")
prediction = pipe("This is some text from a financial report")
print(prediction)
```
## Model description
- Base model: intfloat/multilingual-e5-large
- Task: Document text classification
- Languages: Multilingual
## Training data
- Dataset: DocLayNet-base
- Source: https://huggingface.co/datasets/pierreguillou/DocLayNet-base
- Categories:
```python
{
'financial_reports': 0,
'government_tenders': 1,
'laws_and_regulations': 2,
'manuals': 3,
'patents': 4,
'scientific_articles': 5
}
```
## Training procedure
Trained on single gpu for 2 epochs for apx. 20 minutes.
hyperparameters:
```python
{
'batch_size': 8,
'num_epochs': 10,
'learning_rate': 2e-5,
'weight_decay': 0.01,
'warmup_ratio': 0.1,
'gradient_clip': 1.0,
'label_smoothing': 0.1,
'optimizer': 'AdamW',
'scheduler': 'cosine_with_warmup'
}
```
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