Update README.md

README.md

@@ -46,6 +46,31 @@ widget:
     Pregunta: "Qui és Leo Messi?"
     Resposta:
   example_title: Pregunta-Resposta
+- text: |-
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Me llamo Wolfgang y vivo en Berlin"
+    Entidades: Wolfgang:PER, Berlin:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center"
+    Entidades: parc güell:LOC, barcelona supercomputing center:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Maria y Miguel no tienen ningún problema contigo"
+    Entidades: Maria:PER, Miguel:PER
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Damián se cortó el pelo"
+    Entidades: Damián:PER
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Lo mejor de Barcelona és el bar de mi amigo Pablo"
+    Entidades: Pablo:PER, Barcelona:LOC
+    ----
+    Extrae las entidades nombradas del siguiente texto:
+    Texto: "Carlos comparte piso con Marc"
+    Entidades:
+  example_title: Entidades-Nombradas
 license: apache-2.0
 pipeline_tag: text-generation
 ---
@@ -56,12 +81,11 @@ pipeline_tag: text-generation
 
 This model is a new result towards the long-run problem of "What is the best strategy for training a model in my language (not English)?"
 
-This model adapts the [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) to 2 new target languages Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer before training with 26B tokens in the target language.
+This model adapts the [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) to the new target languages Spanish and Catalan by swapping the tokenizer and adjusting the embedding layer before training with 26B tokens in the target language.
 
 ## Embedding layer adaptation
 
 When adapting a model from English to other languages the tokenizer plays a crucial role. 
-In our case the tokenization of a 
 
 If the tokenizer does not include the target language in its training data, the resulting model will need many more tokens to perform the same task.
 We solve this problem by creating a new tokenizer in the target languages (Spanish and Catalan) and adapting the embedding layer by only reordering the embeddings of the shared tokens of both tokenizers and initializing the rest to the average of all embeddings.
@@ -77,7 +101,14 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
+The model is ready-to-use only for causal language modeling to perform text-generation tasks.
+However, it is intended to be fine-tuned on a generative downstream task.
+
+
+## Limitations and biases
+At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. 
+However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. 
+We intend to conduct research in these areas in the future, and if completed, this model card will be updated. 
 
 ## Training and evaluation data