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---
license: mit
base_model: alexdg19/bert_large_xsum_samsum2
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: bert_large_cnn_daily
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.4251
---
<!-- 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. -->
# bert_large_cnn_daily
This model is a fine-tuned version of [alexdg19/bert_large_xsum_samsum2](https://huggingface.co/alexdg19/bert_large_xsum_samsum2) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7065
- Rouge1: 0.4251
- Rouge2: 0.2024
- Rougel: 0.2992
- Rougelsum: 0.3961
- Gen Len: 60.6232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.6632 | 1.0 | 1021 | 1.6262 | 0.4191 | 0.1992 | 0.2957 | 0.39 | 60.6205 |
| 1.3734 | 2.0 | 2042 | 1.6078 | 0.4253 | 0.2046 | 0.3009 | 0.397 | 61.0692 |
| 1.1497 | 3.0 | 3064 | 1.6759 | 0.4254 | 0.2033 | 0.2998 | 0.3967 | 60.8555 |
| 1.0123 | 4.0 | 4084 | 1.7065 | 0.4251 | 0.2024 | 0.2992 | 0.3961 | 60.6232 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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