Update README.md

README.md

@@ -1,10 +1,40 @@
 ---
 language:
 - en
-- it
+pipeline_tag: text2text-generation
 metrics:
 - f1
-pipeline_tag: text2text-generation
 tags:
-- sql
----
+- grammatical error correction
+- GEC
+- english
+---
+
+This is a fine-tuned version of LLAMA2 trained (7b) on spider, sql-create-context.
+
+To initialize the model:
+
+    
+    #from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+    #model = MBartForConditionalGeneration.from_pretrained("MRNH/mbart-english-grammar-corrector")
+    
+    
+Use the tokenizer:
+
+    
+    #tokenizer = MBart50TokenizerFast.from_pretrained("MRNH/mbart-english-grammar-corrector", src_lang="en_XX", tgt_lang="en_XX")
+    
+    #input = tokenizer("I was here yesterday to studying",
+    #                  text_target="I was here yesterday to study", return_tensors='pt')
+
+To generate text using the model:
+
+    #output = model.generate(input["input_ids"],attention_mask=input["attention_mask"],
+    #                       forced_bos_token_id=tokenizer_it.lang_code_to_id["en_XX"])
+
+
+Training of the model is performed using the following loss computation based on the hidden state output h:
+
+    #h.logits, h.loss = model(input_ids=input["input_ids"],
+    #                                              attention_mask=input["attention_mask"],
+    #                                              labels=input["labels"])