Update README.md

README.md

@@ -8,35 +8,7 @@ library_name: transformers
 pipeline_tag: text-classification
 ---
 This LLaMA-2 7B was fined-tuned on nuclear energy data from twitter/X. The classification accuracy obtained is 96%. \
-This repository is private due to LLaMA-2's usage restrictions. To use it, you need a valid token generated by huggingface. \
+You need access to use the LLaMA-2 model. To use it, you need a valid token generated by huggingface. \
 The number of labels is 3: {0: Negative, 1: Neutral, 2: Positive} \
 Warning: You need sufficient GPU to run this model. 
 
-This is an example to use it, it worked on 8 GB Nvidia-RTX 4060 
-```bash
-from transformers import AutoTokenizer
-from transformers import pipeline
-from transformers import AutoModelForSequenceClassification
-import torch
-
-checkpoint = 'kumo24/llama2-sentiment-nuclear'
-tokenizer=AutoTokenizer.from_pretrained(checkpoint)
-id2label = {0: "negative", 1: "neutral", 2: "positive"}
-label2id = {"negative": 0, "neutral": 1, "positive": 2}
-    
-
-if tokenizer.pad_token is None:
-    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-
-model = AutoModelForSequenceClassification.from_pretrained(checkpoint, 
-                                                       num_labels=3,
-                                                       id2label=id2label, 
-                                                       label2id=label2id,
-                                                       device_map='auto')
-
-sentiment_task = pipeline("sentiment-analysis", 
-                          model=model, 
-                          tokenizer=tokenizer)
-
-print(sentiment_task("Michigan Wolverines are Champions, Go Blue!"))
-```