Add verified text-to-graph input and output examples

README.md

@@ -28,12 +28,25 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 model_path = "Loria-MosAIk/bt5-webnlg-en-ru"
 tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_path).eval()
-inputs = tokenizer("Parse English Paris is located in France.", return_tensors="pt")
+inputs = tokenizer("Parse English Lina Dorado was born in Bogotá in 1975.", return_tensors="pt")
 outputs = model.generate(**inputs, num_beams=5, max_new_tokens=256)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False))
 ```
 
-Use `Parse Russian` for Russian input. Generated triples use `<S>`, `<P>`, and `<O>` markers. The accompanying reproduction code parses these markers and removes exact duplicate triples before evaluation.
+Output:
+
+```text
+<S> Lina Dorado <P> birth year <O> 1975 <S> Lina Dorado <P> birth place <O> Bogotá
+```
+
+For a Russian sentence, use the `Parse Russian` prefix:
+
+```text
+Input:  Parse Russian Валюта Индии - Индийская рупия.
+Output: <S> India <P> currency <O> Indian rupee
+```
+
+The markers `<S>`, `<P>`, and `<O>` identify the subject, relation, and object of each triple. The reproduction code removes exact duplicate triples before evaluation.
 
 The [base model](https://huggingface.co/cointegrated/rut5-base-multitask) is distributed under MIT. The WebNLG data used for fine-tuning are distributed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).