特征提取:常见的TF-IDF(ES就是这个打分机制),word2vec 建模: 主要是AI的算法。 再下面是评估,所以NLP相对复杂些。要具备工程能力把这些不同的模块整合起来。 本篇主要包括:分词(word segmentation)、spell correction(拼写纠错)、stop words removal(停用词)、stemming(标准化) 2 分词 1)主要的分词工具 jieba\ha...
Loading checkpoint shards: 100%|██████████| 9/9 [00:10<00:00, 1.17s/it] 09/23 20:31:55 - OpenCompass - INFO - using stop words: ['<|eot_id|>', '<|end_of_text|>'] 09/23 20:31:55 - OpenCompass - INFO - Start inferencing [llama-3-70b-instruct-hf/ceval-co...
TextRank4Sentence(stop_words_file = './stopword.data' ) # 使用词性过滤,文本小写,使用words_all_filters生成句子之间的相似性 tr4s.train(text = text, speech_tag_filter = True , lower = True , source = 'all_filters' ) print '摘要:' print '\n' .join(tr4s.get_key_sentences(num = 3...
stop_words=kwargs.pop("stop_words", []) fromnexa.generalimportpull_model local_path,run_type=pull_model(model_path,hf) try: ifrun_type=="NLP": fromnexa.gguf.nexa_inference_textimportNexaTextInference @@ -107,6 +108,7 @@ def main(): ...
Let’s define a simple function that counts the number of words in each review: def compute_review_length(example): return {"review_length": len(example["review"].split())} 与前面的lowercase_condition()函数不同,compute_review_length()返回一个字典。该字典的键值在原数据集对象中没有。
alv*_*vas 5 stop-words huggingface-transformers text-generation langchain large-language-model 当我们查看 HuggingFaceHub 模型用法时,langchain有这部分作者不知道如何停止生成,https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_pipeline。 py#L182:...
ifinput_idsinself.keywords:returnTruereturnFalsestop_words = ['}',' }','\n'] stop_ids = [tokenizer.encode(w)forwinstop_words] stop_ids.append(tokenizer.eos_token_id) stop_criteria = KeywordsStoppingCriteria(stop_ids) model.generate( text_inputs='some text:{', StoppingCriteria=stop_...
GenerationConfig validate both constraints and force_words_ids by @FredericOdermatt in #29163 Add generate kwargs to VQA pipeline by @regisss in #29134 Cleaner Cachedtypeanddeviceextraction for CUDA graph generation for quantizers compatibility by @BlackSamorez in #29079 ...
[{'generated_text': "The first shortcut was a long way. The shortest way was the shortest way.\n\nIt was the first time that I had ever used a keyboard, but it was never easy. Just because things felt different and it was hard to put all the stuff right with words couldn't be...
(I won't stop hitting you until you cry!) => 私の人生で車に打たれたことは一度もないと思います。 ( I don't think I've ever been hit by a car in my life, thank goodness!) なっ!何をするだァーッゆるさんッ! (What are you doing?) => 私はバハマへのクルーズに出かけま...