Create README.md

README.md

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+---
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: bert2bert-cnn_dailymail-fp16-finetuned-1.0.0
+  results: []
+datasets:
+- ateneoscsl/BUOD_articlescraper
+- cnn_dailymail
+language:
+- tl
+- en
+---
+
+# 📋  BUOD: bert2bert Transformer Model
+[![Model:Bert2Bert](https://img.shields.io/badge/model-bert2bert-green)](https://huggingface.co/0xhaz/bert2bert-cnn_dailymail-fp16-finetuned-1.0.0)
+This model is a fine-tuned version of [patrickvonplaten/bert2bert-cnn_dailymail-fp16](https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16) on on KAMI-3000 for the task of Filipino Text Summarization.
+Bert2Bert is a EncoderDecoderModel, meaning that both the encoder and the decoder are bert-base-uncased BERT models. 
+
+
+It achieves the following results on the evaluation set:
+- Loss: 2.3346
+- Rouge1: 46.3609
+- Rouge2: 18.8105
+- Rougel: 30.215
+- Rougelsum: 42.3642
+
+## 🔧 Finetuning/ Training procedure
+#### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+#### Training results
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel | Rougelsum |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|
+| 2.8263        | 1.0   | 586  | 2.4478          | 45.3367 | 18.3604 | 29.713 | 41.2805   |
+| 2.1264        | 2.0   | 1172 | 2.3346          | 46.3609 | 18.8105 | 30.215 | 42.3642   |
+
+
+#### Framework versions
+- Transformers 4.26.1
+- Pytorch 1.13.1
+- Datasets 2.10.0
+- Tokenizers 0.13.2