Update README.md

README.md

@@ -14,6 +14,8 @@ language:
 - fa
 library_name: peft
 pipeline_tag: text-classification
+datasets:
+- SeyedAli/Persian-Text-Sentiment
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -21,7 +23,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Persian-Text-Sentiment-Bert-LORA
 
-This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset.
+This model is a Adapter for [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset in Persian Sentment Analysis Task.
 It achieves the following results on the evaluation set:
 - Loss: 0.3427
 - Precision: 0.8579
@@ -40,7 +42,7 @@ This is how to use this model in an example
 from peft import PeftModel
 from transformers import pipeline
 modelname="SeyedAli/Persian-Text-Sentiment-Bert-LORA"
-tokenizer=AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased",model_max_length=100, add_special_tokens = True)
+tokenizer=AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
 model=AutoModelForSequenceClassification.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
 model = PeftModel.from_pretrained(model, modelname)
 pipe = pipeline("text-classification", model=model,tokenizer=tokenizer)