Update README.md

README.md

@@ -1,3 +1,63 @@
 ---
 license: apache-2.0
+language:
+- en
+- zh
 ---
+# SeqGPT-560M
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is SeqGPT-560M weight, a compact model targeting open-domain Natural Language Understanding (NLU). We refer you to our github [repo](https://github.com/Alibaba-NLP/SeqGPT) for more details.
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+The model is fine-tuned based on [BLOOMZ-560M](https://huggingface.co/bigscience/bloomz-560m).
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [SeqGPT](https://github.com/Alibaba-NLP/SeqGPT)
+- **Paper:** [arxiv](https://arxiv.org/abs/2308.10529)
+- **Demo:** [demo](https://www.modelscope.cn/studios/TTCoding/open_ner/summary)
+
+## Uses
+
+
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
+
+model_name_or_path = 'Yirany/SeqGPT-560M'
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
+tokenizer.padding_side = 'left'
+tokenizer.truncation_side = 'left'
+
+if torch.cuda.is_available():
+    model = model.half().cuda()
+model.eval()
+GEN_TOK = '[GEN]'
+
+while True:
+    sent = input('输入/Input: ').strip()
+    task = input('分类/classify press 1, 抽取/extract press 2: ').strip()
+    labels = input('标签集/Label-Set (e.g, labelA,LabelB,LabelC): ').strip().replace(',', '，')
+    task = '分类' if task == '1' else '抽取'
+
+    # Changing the instruction can harm the performance
+    p = '输入: {}\n{}: {}\n输出: {}'.format(sent, task, labels, GEN_TOK)
+    input_ids = tokenizer(p, return_tensors="pt", padding=True, truncation=True, max_length=1024)
+    input_ids = input_ids.to(model.device)
+    outputs = model.generate(**input_ids, num_beams=4, do_sample=False, max_new_tokens=256)
+    input_ids = input_ids.get('input_ids', input_ids)
+    outputs = outputs[0][len(input_ids[0]):]
+    response = tokenizer.decode(outputs, skip_special_tokens=True)
+    print('BOT: ========== \n{}'.format(response))
+```