File size: 1,186 Bytes
a3017d9 ba7cd37 a3017d9 3b188d9 dd5123f a3017d9 ff5b9fd a3017d9 dd5123f a3017d9 ff5b9fd a3017d9 ba7cd37 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
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 |