feat(llama-extract): add client Jul 24, 2024 .pre-commit-config.yaml feat(llama-extract): add client Jul 24, 2024 LICENSE feat(llama-extract): add client Jul 24, 2024 Makefile feat(llama-extract): add client Jul 24, 2024 README.md ...
def call_function_safely(response, function_map): # Extract the function name and arguments from the response tool_call = response['message']['tool_calls'][0] function_name = tool_call['function']['name'] arguments = tool_call['function']['arguments'] # Look up the function in the f...
Extract all entities and the relations between them from the following text. Write the answer as a triple entity1|relationship|entitity2. Do not add anything else. Example Text: Alice is from France. Answer: Alice|is from|France. """ 由于这是一个实验阶段,我将对模型的要求降到最低。我...
# PerformOCRon detected bounding boxesforresultinresults:ocr_results=run_ocr_on_boxes(frame,result.boxes)# Extract and display the textfromOCRresults extracted_text=[detection[1]forocrinocr_resultsfordetectioninocr]print(f"Extracted Text: {', '.join(extracted_text)}") 1. 2. 3. 4. 5. 6...
wiki_text = page["extract"] data_path = Path("data")ifnotdata_path.exists(): Path.mkdir(data_path)withopen(data_path /f"{title}.txt","w")asfp: fp.write(wiki_text) 加载下载的文档 # Load all wiki documentscity_docs = {}forwiki_titleinwiki_titles: ...
断点打上开始调试这里的format和extract函数。对于GLM-4,这次新模型的工具提示词汇现在是由模型本身的tokenizer生成,区别于以往外部组装后进行embedding的做法。此外GLM-4的chat template也存在问题,对比输出中少了系统提示词`` special token; 对于Qwen2,本次新模型支持了多工具调用(单次任务返回多个tool call),之前的...
完整排行榜见extract ☛查看信息抽取能力badcase 8.6 阅读理解阅读理解能力是一种符合能力,考查针对给定信息的理解能力。依据给定信息的种类,可以细分为:文章问答、表格问答、对话问答…… 评测样本举例:牙医:好的,让我们看看你的牙齿。从你的描述和我们的检查结果来看,你可能有一些牙齦疾病,导致牙齿的神经受到刺激,...
if st.button("Extract Terms and Definitions") and document_text: with st.spinner("Extracting..."): extracted_terms = document_text # this is a placeholder! st.write(extracted_terms) 现在我们的应用程序有两个标签,这真的有助于组织。你还注意到我添加了一个默认提示来提取术语——你可以在尝试提...
function_calls = extract_tool_calls(response["replies"][0].content) for function_call in function_calls: # 解析函数调用信息 function_name = function_call["name"] function_args = function_call["arguments"] #找到相应的函数并用给定的参数调用它 ...
Extract all the following values: invoice number, invoice date, remit to company, remit to address, tax ID, invoice to customer, invoice to address, total amount from this invoice: <THE_INVOICE_TEXT> 根据型号的不同,此类提示可能有效,也可能无效。为了获得一个小型的、预先训练的、通用的模型,例如...