Update READM.md

README.md

@@ -7,23 +7,17 @@ licence: apache-2.0
 tags:
 - spanish
 - catalan
-- falcon-7b
+- aguila-7b
 datasets:
 - BSC-LT/open_data_26B_tokens_balanced_es_ca
 metrics:
 - ppl
 model-index:
-- name: falcon_7b_balanced_tokenizer_fp16_CPT_open_data_26B_tokens_balanced_es_ca
+- name: aguila_7b
   results:
   - task:
       name: Causal Language Modeling
       type: text-generation
-    dataset:
-      name: BSC-LT/open_data_26B_tokens_balanced_es_ca
-      type: Causal Language Modeling
-      config: default
-      split: validation
-      args: default
     metrics:
     - name: Perplexity
       type: ppl
@@ -71,7 +65,7 @@ license: apache-2.0
 pipeline_tag: text-generation
 ---
 
-# Cǒndor-7B
+# Ǎguila-7B
 
 ## Table of Contents
 <details>
@@ -97,12 +91,12 @@ pipeline_tag: text-generation
 
 ## Model description
 
-The **Cǒndor-7B** is a transformer-based causal language model for Catalan, Spanish, and English. It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token trilingual corpus collected from publicly available corpora and crawlers.
+The **Ǎguila-7B** is a transformer-based causal language model for Catalan, Spanish, and English. It is based on the [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) model and has been trained on a 26B token trilingual corpus collected from publicly available corpora and crawlers.
 
 
 ## Intended uses and limitations
 
-The **Cǒndor-7B** 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.
+The **Ǎguila-7B** 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.
 
 ## How to use
 
@@ -114,7 +108,7 @@ import transformers
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 input_text = "Maria y Miguel no tienen ningún "
-model = "BSC-LT/condor-7b"
+model = "projecte-aina/aguila-7b"
 tokenizer = AutoTokenizer.from_pretrained(model)
 
 pipeline = transformers.pipeline(