Update README.md

README.md

@@ -1,5 +1,6 @@
 ---
 license: mit
+language: es
 tags:
 - generated_from_trainer
 model-index:
@@ -38,13 +39,42 @@ poema:
   texto: Todos fueron a verle pasar
 ```
 
-## Intended uses & limitations
+### How to use
 
-More information needed
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+model_name = 'hackathon-pln-es/poem-gen-spanish-t5-small'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+author, sentiment, word, start_text = 'Pablo Neruda', 'positivo', 'cielo', 'Todos fueron a la plaza'
+input_text = f"""poema: estilo: {author} && sentimiento: {sentiment} && palabras: {word} && texto: {start_text} """
+inputs = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(inputs["input_ids"],
+                         do_sample = True,
+                         max_length = 30,
+                         repetition_penalty = 20.0,
+                         top_k = 50,
+                         top_p = 0.92)
+detok_outputs = [tokenizer.decode(x, skip_special_tokens=True) for x in outputs]
+res = detok_outputs[0]
+```
 
 ## Training and evaluation data
 
-More information needed
+The original dataset has the columns `author`, `content` and `title`.
+For each poem we generate new examples:
+- content: *line_i* , generated: *line_i+1*
+- content: *concatenate(line_i, line_i+1)* , generated: *line_i+2*
+- content: *concatenate(line_i, line_i+1, line_i+2)* , generated: *line_i+3*
+
+The resulting dataset has the columns `author`, `content`, `title` and `generated`.
+
+For each example we compute the sentiment of the generated column and the nouns. In the case of sentiment, we used the model `mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis` and for nouns extraction we used spaCy.
+ 
 
 ## Training procedure