Update README.md

README.md

@@ -48,13 +48,15 @@ pip install torch>=2.1.0 datasets>=2.17.0 deepspeed>=0.13.4 accelerate>=0.27.2 t
 
 Below is an example using `GNER-T5`
 ```python
+>>> import torch
 >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 >>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-T5-xxl")
->>> model = AutoModelForSeq2SeqLM.from_pretrained("dyyyyyyyy/GNER-T5-xxl")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("dyyyyyyyy/GNER-T5-xxl", torch_dtype=torch.bfloat16).cuda()
 >>> model = model.eval()
 >>> instruction_template = "Please analyze the sentence provided, identifying the type of entity for each word on a token-by-token basis.\nOutput format is: word_1(label_1), word_2(label_2), ...\nWe'll use the BIO-format to label the entities, where:\n1. B- (Begin) indicates the start of a named entity.\n2. I- (Inside) is used for words within a named entity but are not the first word.\n3. O (Outside) denotes words that are not part of a named entity.\n"
+>>> sentence = "did george clooney make a musical in the 1980s"
 >>> entity_labels = ["genre", "rating", "review", "plot", "song", "average ratings", "director", "character", "trailer", "year", "actor", "title"]
->>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(label_list)} and O.\n"
+>>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(entity_labels)} and O.\nSentence: {sentence}"
 >>> inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
 >>> outputs = model.generate(**inputs, max_new_tokens=640)
 >>> response = tokenizer.decode(outputs[0], skip_special_tokens=True)