---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: paper-cutting
  results: []
datasets:
- hidonbush/paper-cuttingv0.1
language:
- en
- zh
metrics:
- accuracy
base_model:
- nvidia/mit-b5
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# paper-cutting

This model was a finetuned version of nvidia/mit-b5 on the paper-cutting datasetv0.1.

It was trained to extract body contents from any resources like articles and books, just like cutting them off the paper.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

paper-cutting v0.1

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0