Update README.md

README.md

@@ -1,3 +1,77 @@
 ---
+language:
+- pt
+thumbnail: Portuguese T5 for the Legal Domain
+tags:
+- transformers
 license: mit
+pipeline_tag: summarization
 ---
+
+
+[![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png)](https://www.inesc-id.pt/projects/PR07005/)
+
+[![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png)](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)
+
+Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/).
+
+Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)
+
+# stjiris/t5-portuguese-legal-summarization
+
+T5 Model fine-tuned over “unicamp-dl/ptt5-base-portuguese-vocab” t5 model.
+
+We utilized various jurisprudence and its summary to train this model.
+
+
+## Usage (HuggingFace transformers)
+```python
+# name of folder principal
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model_checkpoint = "t5_summ_model"
+t5_model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
+t5_tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)
+
+preprocess_text = "These are some big words and text and words and text, again, that we want to summarize"
+t5_prepared_Text = "summarize: "+preprocess_text
+#print ("original text preprocessed: \n", preprocess_text)
+
+tokenized_text = t5_tokenizer.encode(t5_prepared_Text, return_tensors="pt").to(device)
+
+
+# summmarize 
+summary_ids = t5_model.generate(tokenized_text,
+                                    num_beams=4,
+                                    no_repeat_ngram_size=2,
+                                    min_length=512,
+                                    max_length=1024,
+                                    early_stopping=True)
+
+output = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+print ("\n\nSummarized text: \n",output)
+
+```
+
+## Citing & Authors
+
+### Contributions
+[@rufimelo99](https://github.com/rufimelo99)
+
+If you use this work, please cite:
+
+```bibtex
+@inproceedings{MeloSemantic,
+	author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o},
+	title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a},
+}
+
+@article{ptt5_2020,
+  title={PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data},
+  author={Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto},
+  journal={arXiv preprint arXiv:2008.09144},
+  year={2020}
+}
+
+```