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---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
datasets:
- reddit_tifu
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: Bart_reddit_tifu
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: reddit_tifu
type: reddit_tifu
config: long
split: train
args: long
metrics:
- name: Rouge1
type: rouge
value: 0.2709
- name: Precision
type: precision
value: 0.8768
- name: Recall
type: recall
value: 0.8648
- name: F1
type: f1
value: 0.8705
---
<!-- 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. -->
# Bart_reddit_tifu
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the reddit_tifu dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5035
- Rouge1: 0.2709
- Rouge2: 0.0948
- Rougel: 0.2244
- Rougelsum: 0.2244
- Gen Len: 19.3555
- Precision: 0.8768
- Recall: 0.8648
- F1: 0.8705
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 2.6968 | 1.0 | 2370 | 2.5385 | 0.2634 | 0.0907 | 0.218 | 0.2182 | 19.4438 | 0.8766 | 0.8641 | 0.8701 |
| 2.4746 | 2.0 | 4741 | 2.5077 | 0.273 | 0.0941 | 0.2238 | 0.2239 | 19.2572 | 0.8774 | 0.8655 | 0.8712 |
| 2.3066 | 3.0 | 7111 | 2.5012 | 0.2671 | 0.0936 | 0.221 | 0.2211 | 19.3071 | 0.8756 | 0.864 | 0.8696 |
| 2.2041 | 4.0 | 9480 | 2.5035 | 0.2709 | 0.0948 | 0.2244 | 0.2244 | 19.3555 | 0.8768 | 0.8648 | 0.8705 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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