Create README.md (#8760)

model_cards/mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization/README.md

+---
+language: en
+license: apache-2.0
+datasets:
+- cnn_dailymail
+tags:
+- summarization
+---
+
+# Bert-mini2Bert-mini Summarization with 🤗EncoderDecoder Framework
+
+This model is a warm-started *BERT2BERT* ([mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
+
+The model achieves a **16.51** ROUGE-2 score on *CNN/Dailymail*'s test dataset.
+
+For more details on how the model was fine-tuned, please refer to 
+[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.
+
+## Results on test set 📝
+
+| Metric | # Value   |
+| ------ | --------- |
+| **ROUGE-2** | **16.51** |
+
+
+
+## Model in Action 🚀
+
+```python
+from transformers import BertTokenizerFast, EncoderDecoderModel
+import torch
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization')
+model = EncoderDecoderModel.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization').to(device)
+
+def generate_summary(text):
+    # cut off at BERT max length 512
+    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+    input_ids = inputs.input_ids.to(device)
+    attention_mask = inputs.attention_mask.to(device)
+
+    output = model.generate(input_ids, attention_mask=attention_mask)
+
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+  
+text = "your text to be summarized here..."
+generate_summary(text)
+```
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain