---
tags:
- summarization
- fa
- mbert
- mbert2mbert
- Abstractive Summarization
- generated_from_trainer
datasets:
- pn_summary
model-index:
- name: mbert2mbert-finetune-fa
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: xsum
      type: xsum
      config: default
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 0.0
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 0.0
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 0.0
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 0.0
      verified: true
    - name: loss
      type: loss
      value: 11.443371772766113
      verified: true
    - name: gen_len
      type: gen_len
      value: 20.0
      verified: true
---

<!-- 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. -->

# mbert2mbert-finetune-fa

This model is a fine-tuned version of [](https://huggingface.co/) on the pn_summary dataset.

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1