Introduction¶
通过简单示例演示了如何使用 John Snow Labs 的 spark-nlp 预训练模型进行情感和情绪分析。此外，还展示了文本到文本分析（即问答）的简要演示（直接来自 spark-nlp 官方演示）。
需要注意的是，预训练模型和管道的权重是固定的，无法进行微调。通常，我们希望以预训练的深度学习模型为起点，然后针对特定任务进行微调（即迁移学习）。然而，spark-nlp 并不支持这一功能：
以下资源在使用 spark-nlp 时非常有帮助：
- https://www.johnsnowlabs.com/
- https://nlp.johnsnowlabs.com/models
- https://nlp.johnsnowlabs.com/api/python/reference/index.html
- https://nlp.johnsnowlabs.com/api/com/johnsnowlabs/nlp/
- https://nlp.johnsnowlabs.com/demos
- https://github.com/JohnSnowLabs/spark-nlp-workshop
- https://www.johnsnowlabs.com/annotation-lab/
可能的问题¶
如果遇到 notebook 错误，可以考虑以下问题和解决方法：
有时，spark-nlp 下载（在预训练管道或模型中引用）会失败，出现如下错误：
- Py4JError: An error occurred while calling z:com.johnsnowlabs.nlp.pretrained.PythonResourceDownloader.downloadModel
解决方法：
- 方案1: 重启运行环境;重新运行初始化 Spark 上下文的单元格;重新运行 spark-nlp 导入相关的单元格;重新运行抛出错误的下载相关单元格
- 方案2：下载到本地进行加载
如果创建 Spark 会话时没有加载所需的 spark-nlp jar，引用预训练模型或管道会抛出异常 TypeError: 'JavaPackage' object is not callable。（详见 这里。）
当模型只被“部分”下载时，也可能出现奇怪的错误。 可以用如下命令单元格删除模型缓存：
ls -l /root/cache_pretrained/
rm -fr /root/cache_pretrained/
准备 Spark 环境¶
In [ ]:
!pip install pyspark==3.2.1 findspark -i https://mirrors.aliyun.com/pypi/simple/
In [ ]:
# 安装 findspark 以确保可以在本地环境中找到 Spark
import findspark
findspark.init()
In [ ]:
# 该代码框用于设置是否使用自定义 Spark 会话，并根据 CUSTOM_SPARK_SESSION 的值初始化 SparkSession。
# 如果 CUSTOM_SPARK_SESSION 为 True，则后续会使用自定义的 get_spark() 方法创建 Spark 会话（见后续代码）。
# 如果为 False，则直接用默认参数创建 SparkSession 并打印 Spark 版本。
# 设置是否使用自定义 Spark 会话
CUSTOM_SPARK_SESSION = True
from pyspark.sql import SparkSession
if not CUSTOM_SPARK_SESSION:
spark = SparkSession.builder\
.master("local[*]")\
.appName("mylab")\
.config('spark.ui.port', '4050')\
.getOrCreate()
print(f"Spark version: {spark.version}")
依赖项说明（spark-nlp）：¶
In [7]:
!pip install --upgrade pip
!pip install spark-nlp==4.2.4 -i https://mirrors.aliyun.com/pypi/simple/
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: pip in /home/legion/miniconda3/envs/sparknlp424/lib/python3.7/site-packages (22.3.1)
Collecting pip
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8a/6a/19e9fe04fca059ccf770861c7d5721ab4c2aebc539889e97c7977528a53b/pip-24.0-py3-none-any.whl (2.1 MB)
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Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 22.3.1
Uninstalling pip-22.3.1:
Successfully uninstalled pip-22.3.1
Successfully installed pip-24.0
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Collecting spark-nlp==4.2.4
Downloading https://mirrors.aliyun.com/pypi/packages/2c/56/29a8663d25daceb1b2ac18f292725295cace90361a96453b432d00bc5560/spark_nlp-4.2.4-py2.py3-none-any.whl (448 kB)
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Installing collected packages: spark-nlp
Successfully installed spark-nlp-4.2.4
In [ ]:
import warnings
warnings.filterwarnings("ignore")
from pyspark.sql import SparkSession
# 如果需要自定义 SparkSession，则在此处定义相关配置和辅助函数
SPARK_JARS = ["com.johnsnowlabs.nlp:spark-nlp_2.12:4.2.4"]
# 定义自定义 SparkSession 的辅助函数，确保加载 spark-nlp 所需的 jar 包
# jar包的作用：Spark NLP 依赖于特定的 jar 包（Java 库），这些 jar 包包含了 NLP 算法、预训练模型和底层实现。
# 通过在 SparkSession 初始化时加载这些 jar 包，可以确保 Spark 能够识别和使用 spark-nlp 的所有功能。
# 在创建 SparkSession 时通过 .config('spark.jars.packages', ...) 自动下载
def get_spark(master="local[*]", name="mylab"):
builder = SparkSession.builder.appName(name)
builder.config('spark.ui.port', '4050')
builder.config('spark.jars.packages', ",".join(SPARK_JARS))
builder.config("spark.driver.memory", "16G")
builder.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
builder.config("spark.kryoserializer.buffer.max", "2000M")
builder.config("spark.driver.maxResultSize", "0")
return builder.getOrCreate()
if CUSTOM_SPARK_SESSION:
spark = get_spark()
print(f"Spark version: {spark.version}")
Spark version: 3.2.1
Prepare spark-nlp¶
In [ ]:
import sparknlp
from pyspark.ml import Pipeline
from sparknlp.pretrained import PretrainedPipeline
from sparknlp.base import *
from sparknlp.annotator import *
import pandas as pd
spark = sparknlp.start()
# 如果已经存在 Spark 会话，则 sparknlp.start() 会忽略创建新会话。
# 该方法仅加载 spark-nlp 所需的 jar 包，如果需要加载其他 jar 包（如自定义模型或依赖），需使用自定义 SparkSession。
# 推荐在需要加载多个 jar 包或自定义配置时，使用 get_spark() 方法创建 Spark 会话（见上方代码）。
print("Spark NLP version", sparknlp.version())
print("Apache Spark version:", spark.version)
Spark NLP version 4.2.4 Apache Spark version: 3.2.1
In [ ]:
# 定义用于情感分析的文本变量
comment = "The movie I watched today was not a good one"
Pretrained Pipeline¶
- 预训练管道可以直接应用。你也可以为预训练模型自定义 pipeline，具体方法见下节。（这些管线都不支持对预训练模型进行迁移学习。）
Sentiment (general text, Vivek, pipeline)¶
In [11]:
# https://nlp.johnsnowlabs.com/2021/03/24/analyze_sentiment_en.html
sentiment = PretrainedPipeline('analyze_sentiment', lang='en')
result = sentiment.annotate(comment)
result['sentiment']
analyze_sentiment download started this may take some time. Approx size to download 4.9 MB [OK!]
Out[11]:
['negative']
In [10]:
sentiment = PretrainedPipeline.from_disk('analyze_sentiment_en_3.0.0_3.0_1616544471011')
result = sentiment.annotate(comment)
result['sentiment']
Out[10]:
['negative']
In [11]:
sentiment = PipelineModel.load('analyze_sentiment_en_3.0.0_3.0_1616544471011')
comment_df = spark.createDataFrame([(comment,)], ["text"])
result = sentiment.transform(comment_df)
result.select('text', 'sentiment.result').show(truncate=False)
+--------------------------------------------+----------+ |text |result | +--------------------------------------------+----------+ |The movie I watched today was not a good one|[negative]| +--------------------------------------------+----------+
PretrainedPipeline和PipelineModel¶
PretrainedPipeline 是 John Snow Labs 提供的高级 API，允许用户直接加载和应用预训练的 NLP 管道，无需手动构建或训练。它适合快速推理和演示，通常用于单条文本或小批量数据的处理。
PipelineModel 是 Spark ML 的标准管道模型对象，通常由 Pipeline.fit() 训练得到。它可以包含自定义的处理步骤和模型，适合批量数据处理和生产环境部署。对于预训练模型，fit 操作不会改变模型权重，但可以用于自定义数据流和集成。
简而言之：
- PretrainedPipeline：快速加载和应用官方预训练管道，适合简单推理。
- PipelineModel：自定义管道，支持复杂数据流和批量处理，适合生产部署。
Sentiment (IMDB)¶
In [12]:
# https://nlp.johnsnowlabs.com/2021/01/15/analyze_sentimentdl_use_imdb_en.html
sentiment_imdb = PretrainedPipeline('analyze_sentimentdl_use_imdb', lang='en')
result = sentiment_imdb.annotate(comment)
result['sentiment']
analyze_sentimentdl_use_imdb download started this may take some time. Approx size to download 935.7 MB [OK!]
Out[12]:
['pos']
Sentiment (glove)¶
In [13]:
# https://nlp.johnsnowlabs.com/2021/01/15/analyze_sentimentdl_glove_imdb_en.html
sentiment_imdb_glove = PretrainedPipeline('analyze_sentimentdl_glove_imdb', lang='en')
result = sentiment_imdb_glove.annotate(comment)
sentiment_imdb_glove.fullAnnotate(comment)[0]['sentiment']
analyze_sentimentdl_glove_imdb download started this may take some time. Approx size to download 155.3 MB [OK!]
Out[13]:
[Annotation(category, 0, 43, pos, {'sentence': '0', 'pos': '0.99994636', 'neg': '5.3649594E-5'})]
Specify training pipeline for a model¶
- 管道必须使用模型通过
.fit()进行训练后，才能通过.transform()应用 - 管道允许在应用预训练模型或学习算法之前自定义处理步骤。
Download data used to illustrate pipeline.¶
wget -O IMDB-Dataset.csv https://github.com/Ankit152/IMDB-sentiment-analysis/blob/master/IMDB-Dataset.csv?raw=true
In [14]:
data = spark.read.csv("IMDB-Dataset.csv", inferSchema=True, header=True, mode='DROPMALFORMED')
data = data.withColumnRenamed('review', 'text').withColumnRenamed('sentiment', 'sentiment_label')
# pdf = pd.read_csv("IMDB-Dataset.csv")
# data = spark.createDataFrame(pdf).toDF('text', 'sentiment_label')
data.show(truncate=30)
--2022-12-01 21:14:44-- https://github.com/Ankit152/IMDB-sentiment-analysis/blob/master/IMDB-Dataset.csv?raw=true Resolving github.com (github.com)... 20.27.177.113 Connecting to github.com (github.com)|20.27.177.113|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://github.com/Ankit152/IMDB-sentiment-analysis/raw/master/IMDB-Dataset.csv [following] --2022-12-01 21:14:45-- https://github.com/Ankit152/IMDB-sentiment-analysis/raw/master/IMDB-Dataset.csv Reusing existing connection to github.com:443. HTTP request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/Ankit152/IMDB-sentiment-analysis/master/IMDB-Dataset.csv [following] --2022-12-01 21:14:45-- https://raw.githubusercontent.com/Ankit152/IMDB-sentiment-analysis/master/IMDB-Dataset.csv Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 66212309 (63M) [text/plain] Saving to: ‘IMDB-Dataset.csv’ IMDB-Dataset.csv 100%[===================>] 63.14M 172MB/s in 0.4s 2022-12-01 21:14:50 (172 MB/s) - ‘IMDB-Dataset.csv’ saved [66212309/66212309] +------------------------------+---------------+ | text|sentiment_label| +------------------------------+---------------+ |One of the other reviewers ...| positive| |Basically there's a family ...| negative| |I sure would like to see a ...| positive| |This show was an amazing, f...| negative| |Encouraged by the positive ...| negative| |If you like original gut wr...| positive| |"Phil the Alien is one of t...| negative| |I saw this movie when I was...| negative| |The cast played Shakespeare...| negative| |This a fantastic movie of t...| positive| |Kind of drawn in by the ero...| negative| |Some films just simply shou...| positive| |This movie made it into one...| negative| |I remember this film,it was...| positive| |An awful film! It must have...| negative| |After the success of Die Ha...| positive| |What an absolutely stunning...| positive| |This was the worst movie I ...| negative| |The Karen Carpenter Story s...| positive| |This film tried to be too m...| negative| +------------------------------+---------------+ only showing top 20 rows
Sentiment (Vivekn pipeline with data)¶
In [ ]:
# https://nlp.johnsnowlabs.com/2021/11/22/sentiment_vivekn_en.html
document = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
token = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
normalizer = Normalizer() \
.setInputCols(["token"]) \
.setOutputCol("normal")
vivekn = ViveknSentimentModel.pretrained() \
.setInputCols(["document", "normal"]) \
.setOutputCol("result_sentiment")
finisher = Finisher() \
.setInputCols(["result_sentiment"]) \
.setOutputCols("final_sentiment")
pipeline = Pipeline().setStages([document, token, normalizer, vivekn, finisher])
In [ ]:
# pipeline.fit(data) 在使用预训练模型时不会修改模型参数，
# 只是将数据结构和管道步骤绑定起来，生成一个可用于 .transform() 的 PipelineModel。
pipelineModel = pipeline.fit(data)
result = pipelineModel.transform(data)
# 当显示 DataFrame 时，如果某一列的内容长度超过 75 个字符，就会被截断，
# 只显示前 75 个字符，后面用省略号 ... 代替
result.show(truncate=75)
sentiment_vivekn download started this may take some time. Approximate size to download 873.6 KB [OK!] +---------------------------------------------------------------------------+---------------+---------------+ | text|sentiment_label|final_sentiment| +---------------------------------------------------------------------------+---------------+---------------+ |One of the other reviewers has mentioned that after watching just 1 Oz e...| positive| [positive]| |Basically there's a family where a little boy (Jake) thinks there's a zo...| negative| [positive]| |I sure would like to see a resurrection of a up dated Seahunt series wit...| positive| [positive]| |This show was an amazing, fresh & innovative idea in the 70's when it fi...| negative| [positive]| |Encouraged by the positive comments about this film on here I was lookin...| negative| [negative]| |If you like original gut wrenching laughter you will like this movie. If...| positive| [positive]| |"Phil the Alien is one of those quirky films where the humour is based a...| negative| [negative]| |I saw this movie when I was about 12 when it came out. I recall the scar...| negative| [positive]| |The cast played Shakespeare.<br /><br />Shakespeare lost.<br /><br />I a...| negative| [negative]| |This a fantastic movie of three prisoners who become famous. One of the ...| positive| [negative]| |Kind of drawn in by the erotic scenes, only to realize this was one of t...| negative| [negative]| |Some films just simply should not be remade. This is one of them. In and...| positive| [positive]| |This movie made it into one of my top 10 most awful movies. Horrible. <b...| negative| [negative]| |I remember this film,it was the first film i had watched at the cinema t...| positive| [positive]| |An awful film! It must have been up against some real stinkers to be nom...| negative| [positive]| |After the success of Die Hard and it's sequels it's no surprise really t...| positive| [positive]| |What an absolutely stunning movie, if you have 2.5 hrs to kill, watch it...| positive| [positive]| |This was the worst movie I saw at WorldFest and it also received the lea...| negative| [negative]| |The Karen Carpenter Story shows a little more about singer Karen Carpent...| positive| [positive]| |This film tried to be too many things all at once: stinging political sa...| negative| [negative]| +---------------------------------------------------------------------------+---------------+---------------+ only showing top 20 rows
In [47]:
result
Out[47]:
DataFrame[text: string, document: array<struct<annotatorType:string,begin:int,end:int,result:string,metadata:map<string,string>,embeddings:array<float>>>, sentence_embeddings: array<struct<annotatorType:string,begin:int,end:int,result:string,metadata:map<string,string>,embeddings:array<float>>>, sentiment: array<struct<annotatorType:string,begin:int,end:int,result:string,metadata:map<string,string>,embeddings:array<float>>>]
In [46]:
result.printSchema()
root |-- text: string (nullable = true) |-- document: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- annotatorType: string (nullable = true) | | |-- begin: integer (nullable = false) | | |-- end: integer (nullable = false) | | |-- result: string (nullable = true) | | |-- metadata: map (nullable = true) | | | |-- key: string | | | |-- value: string (valueContainsNull = true) | | |-- embeddings: array (nullable = true) | | | |-- element: float (containsNull = false) |-- sentence_embeddings: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- annotatorType: string (nullable = true) | | |-- begin: integer (nullable = false) | | |-- end: integer (nullable = false) | | |-- result: string (nullable = true) | | |-- metadata: map (nullable = true) | | | |-- key: string | | | |-- value: string (valueContainsNull = true) | | |-- embeddings: array (nullable = true) | | | |-- element: float (containsNull = false) |-- sentiment: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- annotatorType: string (nullable = true) | | |-- begin: integer (nullable = false) | | |-- end: integer (nullable = false) | | |-- result: string (nullable = true) | | |-- metadata: map (nullable = true) | | | |-- key: string | | | |-- value: string (valueContainsNull = true) | | |-- embeddings: array (nullable = true) | | | |-- element: float (containsNull = false)
应用由管道创建的“已训练”模型。¶
请注意，对于预训练模型，情感模型的权重是固定的。
In [16]:
# apply trained model to text
example = spark.createDataFrame([[comment]]).toDF("text")
pipelineModel.transform(example).show(truncate=False)
+--------------------------------------------+---------------+ |text |final_sentiment| +--------------------------------------------+---------------+ |The movie I watched today was not a good one|[negative] | +--------------------------------------------+---------------+
评估已训练的模型¶
- 注意，拟合预训练模型并不会改变模型，如下所示。
In [ ]:
from sklearn.metrics import classification_report, accuracy_score
df2 = pipelineModel.transform(data).selectExpr('sentiment_label','text',"final_sentiment as result").toPandas()
df2['result'] = df2['result'].apply(lambda x: x[0])
# 这里的 apply 函数用于将 'result' 列中的列表（如 ['positive']）提取为单个字符串（如 'positive'），方便后续评估
print(classification_report(df2.sentiment_label, df2.result))
print(accuracy_score(df2.sentiment_label, df2.result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
precision recall f1-score support
,positive 0.00 0.00 0.00 2
negative 0.53 0.62 0.57 13792
positive 0.58 0.49 0.53 14897
accuracy 0.55 28691
macro avg 0.37 0.37 0.37 28691
weighted avg 0.56 0.55 0.55 28691
0.5523334843679203
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
评估未训练的模型¶
- 下例说明，将已训练模型包含在管道中（并运行 fit）不会改变已训练模型。因此，模型训练（上方）和未训练（下方）时的结果是一样的。
In [18]:
# Fit to no data (thus no "training")
empty_data = spark.createDataFrame([['']]).toDF("text")
pipelineModel = pipeline.fit(empty_data)
In [19]:
# apply same pipeline, but model has not been "trained"
example = spark.createDataFrame([[comment]]).toDF("text")
pipelineModel.transform(example).show(truncate=False)
+--------------------------------------------+---------------+ |text |final_sentiment| +--------------------------------------------+---------------+ |The movie I watched today was not a good one|[negative] | +--------------------------------------------+---------------+
In [20]:
# Same result as pipeline without "training"
from sklearn.metrics import classification_report, accuracy_score
df2 = pipelineModel.transform(data).selectExpr('sentiment_label','text',"final_sentiment as result").toPandas()
df2['result'] = df2['result'].apply(lambda x: x[0])
print(classification_report(df2.sentiment_label, df2.result))
print(accuracy_score(df2.sentiment_label, df2.result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
precision recall f1-score support
,positive 0.00 0.00 0.00 2
negative 0.53 0.62 0.57 13792
positive 0.58 0.49 0.53 14897
accuracy 0.55 28691
macro avg 0.37 0.37 0.37 28691
weighted avg 0.56 0.55 0.55 28691
0.5523334843679203
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result))
使用 XLNet 进行情感分析（IMDB trained）¶
- spark-nlp 提供的最佳预训练情感分析模型之一。
- 该模型在斯坦福 IMDB 电影评论数据集（25,000 条评论）上训练。
- 支持英文文本的情感分类任务，能够区分正面和负面情感。
- 适用于大规模文本情感分析场景。
In [ ]:
# https://nlp.johnsnowlabs.com/2021/12/23/xlnet_base_sequence_classifier_imdb_en.html
# https://huggingface.co/datasets/imdb (Trained on Stanford sentimetn 25,000 movies)
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
sequenceClassifier = XlnetForSequenceClassification \
.pretrained('xlnet_base_sequence_classifier_imdb', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setCaseSensitive(False) \
.setMaxSentenceLength(512)
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
# Fit to data
pipelineModel = pipeline.fit(data)
result = pipelineModel.transform(data)
result.show(truncate=False)
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Approximate size to download 419.7 MB
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|One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side. |positive |[{document, 0, 1760, One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side., {sentence -> 0}, []}] |[{token, 0, 2, One, {sentence -> 0}, []}, {token, 4, 5, of, {sentence -> 0}, []}, {token, 7, 9, the, {sentence -> 0}, []}, {token, 11, 15, other, {sentence -> 0}, []}, {token, 17, 25, reviewers, {sentence -> 0}, []}, {token, 27, 29, has, {sentence -> 0}, []}, {token, 31, 39, mentioned, {sentence -> 0}, []}, {token, 41, 44, that, {sentence -> 0}, []}, {token, 46, 50, after, {sentence -> 0}, []}, {token, 52, 59, watching, {sentence -> 0}, []}, {token, 61, 64, just, {sentence -> 0}, []}, {token, 66, 66, 1, {sentence -> 0}, []}, {token, 68, 69, Oz, {sentence -> 0}, []}, {token, 71, 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|Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet & his parents are fighting all the time.<br /><br />This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.<br /><br />OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing & arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.<br /><br />3 out of 10 just for the well playing parents & descent dialogs. As for the shots with Jake: just ignore them. |negative |[{document, 0, 747, Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet & his parents are fighting all the time.<br /><br />This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.<br /><br />OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing & arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.<br /><br />3 out of 10 just for the well playing parents & descent dialogs. 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|I sure would like to see a resurrection of a up dated Seahunt series with the tech they have today it would bring back the kid excitement in me.I grew up on black and white TV and Seahunt with Gunsmoke were my hero's every week.You have my vote for a comeback of a new sea hunt.We need a change of pace in TV and this would work for a world of under water adventure.Oh by the way thank you for an outlet like this to view many viewpoints about TV and the many movies.So any ole way I believe I've got what I wanna say.Would be nice to read some more plus points about sea hunt.If my rhymes would be 10 lines would you let me submit,or leave me out to be in doubt and have me to quit,If this is so then I must go so lets do it. |positive |[{document, 0, 725, I sure would like to see a resurrection of a up dated Seahunt series with the tech they have today it would bring back the kid excitement in me.I grew up on black and white TV and Seahunt with Gunsmoke were my hero's every week.You have my 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|This show was an amazing, fresh & innovative idea in the 70's when it first aired. The first 7 or 8 years were brilliant, but things dropped off after that. By 1990, the show was not really funny anymore, and it's continued its decline further to the complete waste of time it is today.<br /><br />It's truly disgraceful how far this show has fallen. The writing is painfully bad, the performances are almost as bad - if not for the mildly entertaining respite of the guest-hosts, this show probably wouldn't still be on the air. I find it so hard to believe that the same creator that hand-selected the original cast also chose the band of hacks that followed. How can one recognize such brilliance and then see fit to replace it with such mediocrity? I felt I must give 2 stars out of respect for the original cast that made this show such a huge success. As it is now, the show is just awful. 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|Encouraged by the positive comments about this film on here I was looking forward to watching this film. Bad mistake. I've seen 950+ films and this is truly one of the worst of them - it's awful in almost every way: editing, pacing, storyline, 'acting,' soundtrack (the film's only song - a lame country tune - is played no less than four times). The film looks cheap and nasty and is boring in the extreme. Rarely have I been so happy to see the end credits of a film. <br /><br />The only thing that prevents me giving this a 1-score is Harvey Keitel - while this is far from his best performance he at least seems to be making a bit of an effort. One for Keitel obsessives only. |negative |[{document, 0, 680, Encouraged by the positive comments about this film on here I was looking forward to watching this film. Bad mistake. I've seen 950+ films and this is truly one of the worst of them - it's awful in almost every way: editing, pacing, storyline, 'acting,' soundtrack (the film's only song - a lame country tune - is played no less than four times). The film looks cheap and nasty and is boring in the extreme. Rarely have I been so happy to see the end credits of a film. <br /><br />The only thing that prevents me giving this a 1-score is Harvey Keitel - while this is far from his best performance he at least seems to be making a bit of an effort. 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|If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.<br /><br />Great Camp!!! |positive |[{document, 0, 175, If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.<br /><br />Great Camp!!!, {sentence -> 0}, []}] |[{token, 0, 1, If, {sentence -> 0}, []}, {token, 3, 5, you, {sentence -> 0}, []}, {token, 7, 10, like, {sentence -> 0}, []}, {token, 12, 19, original, {sentence -> 0}, []}, {token, 21, 23, gut, {sentence -> 0}, []}, {token, 25, 33, wrenching, {sentence -> 0}, []}, {token, 35, 42, laughter, {sentence -> 0}, []}, {token, 44, 46, you, {sentence -> 0}, []}, {token, 48, 51, will, {sentence -> 0}, []}, {token, 53, 56, like, {sentence -> 0}, []}, {token, 58, 61, this, {sentence -> 0}, []}, {token, 63, 67, movie, {sentence -> 0}, []}, {token, 68, 68, ., {sentence -> 0}, []}, {token, 70, 71, If, {sentence -> 0}, []}, {token, 73, 75, you, {sentence -> 0}, []}, {token, 77, 79, are, {sentence -> 0}, []}, {token, 81, 85, young, {sentence -> 0}, []}, {token, 87, 88, or, {sentence -> 0}, []}, {token, 90, 92, old, {sentence -> 0}, []}, {token, 94, 97, then, {sentence -> 0}, []}, {token, 99, 101, you, {sentence -> 0}, []}, {token, 103, 106, will, {sentence -> 0}, []}, {token, 108, 111, love, {sentence -> 0}, []}, {token, 113, 116, this, {sentence -> 0}, []}, {token, 118, 122, movie, {sentence -> 0}, []}, {token, 123, 123, ,, {sentence -> 0}, []}, {token, 125, 128, hell, {sentence -> 0}, []}, {token, 130, 133, even, {sentence -> 0}, []}, {token, 135, 136, my, {sentence -> 0}, []}, {token, 138, 140, mom, {sentence -> 0}, []}, {token, 142, 146, liked, {sentence -> 0}, []}, {token, 148, 153, it.<br, {sentence -> 0}, []}, {token, 155, 159, /><br, {sentence -> 0}, []}, {token, 161, 167, />Great, {sentence -> 0}, []}, {token, 169, 172, Camp, {sentence -> 0}, []}, {token, 173, 175, !!!, {sentence -> 0}, []}] |[{category, 0, 175, pos, {sentence -> 0, Some(neg) -> 0.0014877004, Some(pos) -> 0.9985123}, []}] |
|"Phil the Alien is one of those quirky films where the humour is based around the oddness of everything rather than actual punchlines.<br /><br />At first it was very odd and pretty funny but as the movie progressed I didn't find the jokes or oddness funny anymore.<br /><br />Its a low budget film (thats never a problem in itself), there were some pretty interesting characters, but eventually I just lost interest.<br /><br />I imagine this film would appeal to a stoner who is currently partaking.<br /><br />For something similar but better try ""Brother from another planet""" |negative |[{document, 0, 581, "Phil the Alien is one of those quirky films where the humour is based around the oddness of everything rather than actual punchlines.<br /><br />At first it was very odd and pretty funny but as the movie progressed I didn't find the jokes or oddness funny anymore.<br /><br />Its a low budget film (thats never a problem in itself), there were some pretty interesting characters, but 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|I saw this movie when I was about 12 when it came out. I recall the scariest scene was the big bird eating men dangling helplessly from parachutes right out of the air. The horror. The horror.<br /><br />As a young kid going to these cheesy B films on Saturday afternoons, I still was tired of the formula for these monster type movies that usually included the hero, a beautiful woman who might be the daughter of a professor and a happy resolution when the monster died in the end. I didn't care much for the romantic angle as a 12 year old and the predictable plots. I love them now for the unintentional humor.<br /><br />But, about a year or so later, I saw Psycho when it came out and I loved that the star, Janet Leigh, was bumped off early in the film. I sat up and took notice at that point. Since screenwriters are making up the story, make it up to be as scary as possible and not from a well-worn formula. There are no rules. |negative |[{document, 0, 936, I saw this movie when I was about 12 when it came out. I recall the scariest scene was the big bird eating men dangling helplessly from parachutes right out of the air. The horror. The horror.<br /><br />As a young kid going to these cheesy B films on Saturday afternoons, I still was tired of the formula for these monster type movies that usually included the hero, a beautiful woman who might be the daughter of a professor and a happy resolution when the monster died in the end. I didn't care much for the romantic angle as a 12 year old and the predictable plots. I love them now for the unintentional humor.<br /><br />But, about a year or so later, I saw Psycho when it came out and I loved that the star, Janet Leigh, was bumped off early in the film. I sat up and took notice at that point. Since screenwriters are making up the story, make it up to be as scary as possible and not from a well-worn formula. 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|The cast played Shakespeare.<br /><br />Shakespeare lost.<br /><br />I appreciate that this is trying to bring Shakespeare to the masses, but why ruin something so good.<br /><br />Is it because 'The Scottish Play' is my favorite Shakespeare? I do not know. What I do know is that a certain Rev Bowdler (hence bowdlerization) tried to do something similar in the Victorian era.<br /><br />In other words, you cannot improve perfection.<br /><br />I have no more to write but as I have to write at least ten lines of text (and English composition was never my forte I will just have to keep going and say that this movie, as the saying goes, just does not cut it. |negative |[{document, 0, 661, The cast played Shakespeare.<br /><br />Shakespeare lost.<br /><br />I appreciate that this is trying to bring Shakespeare to the masses, but why ruin something so good.<br /><br />Is it because 'The Scottish Play' is my favorite Shakespeare? I do not know. 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|This a fantastic movie of three prisoners who become famous. One of the actors is george clooney and I'm not a fan but this roll is not bad. Another good thing about the movie is the soundtrack (The man of constant sorrow). I recommand this movie to everybody. Greetings Bart |positive |[{document, 0, 274, This a fantastic movie of three prisoners who become famous. One of the actors is george clooney and I'm not a fan but this roll is not bad. Another good thing about the movie is the soundtrack (The man of constant sorrow). I recommand this movie to everybody. Greetings Bart, {sentence -> 0}, []}] |[{token, 0, 3, This, {sentence -> 0}, []}, {token, 5, 5, a, {sentence -> 0}, []}, {token, 7, 15, fantastic, {sentence -> 0}, []}, {token, 17, 21, movie, {sentence -> 0}, []}, {token, 23, 24, of, {sentence -> 0}, []}, {token, 26, 30, three, {sentence -> 0}, []}, {token, 32, 40, prisoners, {sentence -> 0}, []}, {token, 42, 44, who, {sentence -> 0}, []}, {token, 46, 51, become, {sentence -> 0}, []}, {token, 53, 58, famous, {sentence -> 0}, []}, {token, 59, 59, ., {sentence -> 0}, []}, {token, 61, 63, One, {sentence -> 0}, []}, {token, 65, 66, of, {sentence -> 0}, []}, {token, 68, 70, the, {sentence -> 0}, []}, {token, 72, 77, actors, {sentence -> 0}, []}, {token, 79, 80, is, {sentence -> 0}, []}, {token, 82, 87, george, {sentence -> 0}, []}, {token, 89, 95, clooney, {sentence -> 0}, []}, {token, 97, 99, and, {sentence -> 0}, []}, {token, 101, 103, I'm, {sentence -> 0}, []}, {token, 105, 107, not, {sentence -> 0}, []}, {token, 109, 109, a, {sentence -> 0}, []}, {token, 111, 113, fan, {sentence -> 0}, []}, {token, 115, 117, but, {sentence -> 0}, []}, {token, 119, 122, this, {sentence -> 0}, []}, {token, 124, 127, roll, {sentence -> 0}, []}, {token, 129, 130, is, {sentence -> 0}, []}, {token, 132, 134, not, {sentence -> 0}, []}, {token, 136, 138, bad, {sentence -> 0}, []}, {token, 139, 139, ., {sentence -> 0}, []}, {token, 141, 147, Another, {sentence -> 0}, []}, {token, 149, 152, good, {sentence -> 0}, []}, {token, 154, 158, thing, {sentence -> 0}, []}, {token, 160, 164, about, {sentence -> 0}, []}, {token, 166, 168, the, {sentence -> 0}, []}, {token, 170, 174, movie, {sentence -> 0}, []}, {token, 176, 177, is, {sentence -> 0}, []}, {token, 179, 181, the, {sentence -> 0}, []}, {token, 183, 192, soundtrack, {sentence -> 0}, []}, {token, 194, 194, (, {sentence -> 0}, []}, {token, 195, 197, The, {sentence -> 0}, []}, {token, 199, 201, man, {sentence -> 0}, []}, {token, 203, 204, of, {sentence -> 0}, []}, {token, 206, 213, constant, {sentence -> 0}, []}, {token, 215, 220, sorrow, {sentence -> 0}, []}, {token, 221, 222, )., {sentence -> 0}, []}, {token, 224, 224, I, {sentence -> 0}, []}, {token, 226, 234, recommand, {sentence -> 0}, []}, {token, 236, 239, this, {sentence -> 0}, []}, {token, 241, 245, movie, {sentence -> 0}, []}, {token, 247, 248, to, {sentence -> 0}, []}, {token, 250, 258, everybody, {sentence -> 0}, []}, {token, 259, 259, ., {sentence -> 0}, []}, {token, 261, 269, Greetings, {sentence -> 0}, []}, {token, 271, 274, Bart, {sentence -> 0}, []}] |[{category, 0, 274, pos, {sentence -> 0, Some(neg) -> 5.219826E-4, Some(pos) -> 0.99947804}, []}] |
|Kind of drawn in by the erotic scenes, only to realize this was one of the most amateurish and unbelievable bits of film I've ever seen. Sort of like a high school film project. What was Rosanna Arquette thinking?? And what was with all those stock characters in that bizarre supposed Midwest town? Pretty hard to get involved with this one. No lessons to be learned from it, no brilliant insights, just stilted and quite ridiculous (but lots of skin, if that intrigues you) videotaped nonsense....What was with the bisexual relationship, out of nowhere, after all the heterosexual encounters. And what was with that absurd dance, with everybody playing their stereotyped roles? Give this one a pass, it's like a million other miles of bad, wasted film, money that could have been spent on starving children or Aids in Africa..... |negative |[{document, 0, 829, Kind of drawn in by the erotic scenes, only to realize this was one of the most amateurish and unbelievable bits of film I've ever seen. Sort of like a high school film project. What was Rosanna Arquette thinking?? And what was with all those stock characters in that bizarre supposed Midwest town? Pretty hard to get involved with this one. No lessons to be learned from it, no brilliant insights, just stilted and quite ridiculous (but lots of skin, if that intrigues you) videotaped nonsense....What was with the bisexual relationship, out of nowhere, after all the heterosexual encounters. And what was with that absurd dance, with everybody playing their stereotyped roles? 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|Some films just simply should not be remade. This is one of them. In and of itself it is not a bad film. But it fails to capture the flavor and the terror of the 1963 film of the same title. Liam Neeson was excellent as he always is, and most of the cast holds up, with the exception of Owen Wilson, who just did not bring the right feel to the character of Luke. But the major fault with this version is that it strayed too far from the Shirley Jackson story in it's attempts to be grandiose and lost some of the thrill of the earlier film in a trade off for snazzier special effects. Again I will say that in and of itself it is not a bad film. But you will enjoy the friction of terror in the older version much more. |positive |[{document, 0, 719, Some films just simply should not be remade. This is one of them. In and of itself it is not a bad film. But it fails to capture the flavor and the terror of the 1963 film of the same title. Liam Neeson was excellent as he always is, and most of the cast holds up, with the exception of Owen Wilson, who just did not bring the right feel to the character of Luke. But the major fault with this version is that it strayed too far from the Shirley Jackson story in it's attempts to be grandiose and lost some of the thrill of the earlier film in a trade off for snazzier special effects. Again I will say that in and of itself it is not a bad film. 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|This movie made it into one of my top 10 most awful movies. Horrible. <br /><br />There wasn't a continuous minute where there wasn't a fight with one monster or another. There was no chance for any character development, they were too busy running from one sword fight to another. I had no emotional attachment (except to the big bad machine that wanted to destroy them) <br /><br />Scenes were blatantly stolen from other movies, LOTR, Star Wars and Matrix. <br /><br />Examples<br /><br />>The ghost scene at the end was stolen from the final scene of the old Star Wars with Yoda, Obee One and Vader. <br /><br />>The spider machine in the beginning was exactly like Frodo being attacked by the spider in Return of the Kings. (Elijah Wood is the victim in both films) and wait......it hypnotizes (stings) its victim and wraps them up.....uh hello????<br /><br />>And the whole machine vs. humans theme WAS the Matrix..or Terminator.....<br /><br />There are more examples but why waste the time? And will someone tell me what was with the Nazi's?!?! Nazi's???? <br /><br />There was a juvenile story line rushed to a juvenile conclusion. The movie could not decide if it was a children's movie or an adult movie and wasn't much of either. <br /><br />Just awful. A real disappointment to say the least. Save your money. |negative |[{document, 0, 1321, This movie made it into one of my top 10 most awful movies. Horrible. <br /><br />There wasn't a continuous minute where there wasn't a fight with one monster or another. There was no chance for any character development, they were too busy running from one sword fight to another. I had no emotional attachment (except to the big bad machine that wanted to destroy them) <br /><br />Scenes were blatantly stolen from other movies, LOTR, Star Wars and Matrix. <br /><br />Examples<br /><br />>The ghost scene at the end was stolen from the final scene of the old Star Wars with Yoda, Obee One and Vader. <br /><br />>The spider machine in the beginning was exactly like Frodo being attacked by the spider in Return of the Kings. (Elijah Wood is the victim in both films) and wait......it hypnotizes (stings) its victim and wraps them up.....uh hello????<br /><br />>And the whole machine vs. humans theme WAS the Matrix..or Terminator.....<br /><br />There are more examples but why waste the time? And will someone tell me what was with the Nazi's?!?! Nazi's???? <br /><br />There was a juvenile story line rushed to a juvenile conclusion. The movie could not decide if it was a children's movie or an adult movie and wasn't much of either. <br /><br />Just awful. A real disappointment to say the least. 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|I remember this film,it was the first film i had watched at the cinema the picture was dark in places i was very nervous it was back in 74/75 my Dad took me my brother & sister to Newbury cinema in Newbury Berkshire England. I recall the tigers and the lots of snow in the film also the appearance of Grizzly Adams actor Dan Haggery i think one of the tigers gets shot and dies. If anyone knows where to find this on DVD etc please let me know.The cinema now has been turned in a fitness club which is a very big shame as the nearest cinema now is 20 miles away, would love to hear from others who have seen this film or any other like it. |positive |[{document, 0, 638, I remember this film,it was the first film i had watched at the cinema the picture was dark in places i was very nervous it was back in 74/75 my Dad took me my brother & sister to Newbury cinema in Newbury Berkshire England. 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|An awful film! It must have been up against some real stinkers to be nominated for the Golden Globe. They've taken the story of the first famous female Renaissance painter and mangled it beyond recognition. My complaint is not that they've taken liberties with the facts; if the story were good, that would perfectly fine. But it's simply bizarre -- by all accounts the true story of this artist would have made for a far better film, so why did they come up with this dishwater-dull script? I suppose there weren't enough naked people in the factual version. It's hurriedly capped off in the end with a summary of the artist's life -- we could have saved ourselves a couple of hours if they'd favored the rest of the film with same brevity. |negative |[{document, 0, 740, An awful film! It must have been up against some real stinkers to be nominated for the Golden Globe. They've taken the story of the first famous female Renaissance painter and mangled it beyond recognition. My complaint is not that they've taken liberties with the facts; if the story were good, that would perfectly fine. But it's simply bizarre -- by all accounts the true story of this artist would have made for a far better film, so why did they come up with this dishwater-dull script? I suppose there weren't enough naked people in the factual version. 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|After the success of Die Hard and it's sequels it's no surprise really that in the 1990s, a glut of 'Die Hard on a .....' movies cashed in on the wrong guy, wrong place, wrong time concept. That is what they did with Cliffhanger, Die Hard on a mountain just in time to rescue Sly 'Stop or My Mom Will Shoot' Stallone's career.<br /><br />Cliffhanger is one big nit-pickers dream, especially to those who are expert at mountain climbing, base-jumping, aviation, facial expressions, acting skills. All in all it's full of excuses to dismiss the film as one overblown pile of junk. Stallone even managed to get out-acted by a horse! However, if you an forget all the nonsense, it's actually a very lovable and undeniably entertaining romp that delivers as plenty of thrills, and unintentionally, plenty of laughs.<br /><br />You've got to love John Lithgows sneery evilness, his tick every box band of baddies, and best of all, the permanently harassed and hapless 'turncoat' agent, Rex Linn as Travers.<br /><br />He may of been Henry in 'Portrait of a Serial Killer' but Michael Rooker is noteworthy for a cringe-worthy performance as Hal, he insists on constantly shrieking in painful disbelief at his captors 'that man never hurt anybody' And whilst he surely can't be, it really does look like Ralph Waite's Frank character is grinning as the girl plummets to her death.<br /><br />Mention too must go to former 'London's Burning' actor Craig Fairbrass as the Brit bad guy, who comes a cropper whilst using Hal as a Human Football, yes, you can't help enjoy that bit, Hal needed a good kicking.<br /><br />So forget your better judgement, who cares if 'that could never happen', lower your acting expectations, turn up the volume and enjoy! And if you're looking for Qaulen, he's the one wearing the helicopter.|positive |[{document, 0, 1812, After the success of Die Hard and it's sequels it's no surprise really that in the 1990s, a glut of 'Die Hard on a .....' movies cashed in on the wrong guy, wrong place, wrong time concept. That is what they did with Cliffhanger, Die Hard on a mountain just in time to rescue Sly 'Stop or My Mom Will Shoot' Stallone's career.<br /><br />Cliffhanger is one big nit-pickers dream, especially to those who are expert at mountain climbing, base-jumping, aviation, facial expressions, acting skills. All in all it's full of excuses to dismiss the film as one overblown pile of junk. Stallone even managed to get out-acted by a horse! However, if you an forget all the nonsense, it's actually a very lovable and undeniably entertaining romp that delivers as plenty of thrills, and unintentionally, plenty of laughs.<br /><br />You've got to love John Lithgows sneery evilness, his tick every box band of baddies, and best of all, the permanently harassed and hapless 'turncoat' agent, Rex Linn as Travers.<br /><br />He may of been Henry in 'Portrait of a Serial Killer' but Michael Rooker is noteworthy for a cringe-worthy performance as Hal, he insists on constantly shrieking in painful disbelief at his captors 'that man never hurt anybody' And whilst he surely can't be, it really does look like Ralph Waite's Frank character is grinning as the girl plummets to her death.<br /><br />Mention too must go to former 'London's Burning' actor Craig Fairbrass as the Brit bad guy, who comes a cropper whilst using Hal as a Human Football, yes, you can't help enjoy that bit, Hal needed a good kicking.<br /><br />So forget your better judgement, who cares if 'that could never happen', lower your acting expectations, turn up the volume and enjoy! 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|What an absolutely stunning movie, if you have 2.5 hrs to kill, watch it, you won't regret it, it's too much fun! Rajnikanth carries the movie on his shoulders and although there isn't anything more other than him, I still liked it. The music by A.R.Rehman takes time to grow on you but after you heard it a few times, you really start liking it. |positive |[{document, 0, 345, What an absolutely stunning movie, if you have 2.5 hrs to kill, watch it, you won't regret it, it's too much fun! Rajnikanth carries the movie on his shoulders and although there isn't anything more other than him, I still liked it. The music by A.R.Rehman takes time to grow on you but after you heard it a few times, you really start liking it., {sentence -> 0}, []}] |[{token, 0, 3, What, {sentence -> 0}, []}, {token, 5, 6, an, {sentence -> 0}, []}, {token, 8, 17, absolutely, {sentence -> 0}, []}, {token, 19, 26, stunning, {sentence -> 0}, []}, {token, 28, 32, movie, {sentence -> 0}, []}, {token, 33, 33, ,, {sentence -> 0}, []}, {token, 35, 36, if, {sentence -> 0}, []}, {token, 38, 40, you, {sentence -> 0}, []}, {token, 42, 45, have, {sentence -> 0}, []}, {token, 47, 49, 2.5, {sentence -> 0}, []}, {token, 51, 53, hrs, {sentence -> 0}, []}, {token, 55, 56, to, {sentence -> 0}, []}, {token, 58, 61, kill, {sentence -> 0}, []}, {token, 62, 62, ,, {sentence -> 0}, []}, {token, 64, 68, watch, {sentence -> 0}, []}, {token, 70, 71, it, {sentence -> 0}, []}, {token, 72, 72, ,, {sentence -> 0}, []}, {token, 74, 76, you, {sentence -> 0}, []}, {token, 78, 82, won't, {sentence -> 0}, []}, {token, 84, 89, regret, {sentence -> 0}, []}, {token, 91, 92, it, {sentence -> 0}, []}, {token, 93, 93, ,, {sentence -> 0}, []}, {token, 95, 98, it's, {sentence -> 0}, []}, {token, 100, 102, too, {sentence -> 0}, []}, {token, 104, 107, much, {sentence -> 0}, []}, {token, 109, 111, fun, {sentence -> 0}, []}, {token, 112, 112, !, {sentence -> 0}, []}, {token, 114, 123, Rajnikanth, {sentence -> 0}, []}, {token, 125, 131, carries, {sentence -> 0}, []}, {token, 133, 135, the, {sentence -> 0}, []}, {token, 137, 141, movie, {sentence -> 0}, []}, {token, 143, 144, on, {sentence -> 0}, []}, {token, 146, 148, his, {sentence -> 0}, []}, {token, 150, 158, shoulders, {sentence -> 0}, []}, {token, 160, 162, and, {sentence -> 0}, []}, {token, 164, 171, although, {sentence -> 0}, []}, {token, 173, 177, there, {sentence -> 0}, []}, {token, 179, 183, isn't, {sentence -> 0}, []}, {token, 185, 192, anything, {sentence -> 0}, []}, {token, 194, 197, more, {sentence -> 0}, []}, {token, 199, 203, other, {sentence -> 0}, []}, {token, 205, 208, than, {sentence -> 0}, []}, {token, 210, 212, him, {sentence -> 0}, []}, {token, 213, 213, ,, {sentence -> 0}, []}, {token, 215, 215, I, {sentence -> 0}, []}, {token, 217, 221, still, {sentence -> 0}, []}, {token, 223, 227, liked, {sentence -> 0}, []}, {token, 229, 230, it, {sentence -> 0}, []}, {token, 231, 231, ., {sentence -> 0}, []}, {token, 233, 235, The, {sentence -> 0}, []}, {token, 237, 241, music, {sentence -> 0}, []}, {token, 243, 244, by, {sentence -> 0}, []}, {token, 246, 255, A.R.Rehman, {sentence -> 0}, []}, {token, 257, 261, takes, {sentence -> 0}, []}, {token, 263, 266, time, {sentence -> 0}, []}, {token, 268, 269, to, {sentence -> 0}, []}, {token, 271, 274, grow, {sentence -> 0}, []}, {token, 276, 277, on, {sentence -> 0}, []}, {token, 279, 281, you, {sentence -> 0}, []}, {token, 283, 285, but, {sentence -> 0}, []}, {token, 287, 291, after, {sentence -> 0}, []}, {token, 293, 295, you, {sentence -> 0}, []}, {token, 297, 301, heard, {sentence -> 0}, []}, {token, 303, 304, it, {sentence -> 0}, []}, {token, 306, 306, a, {sentence -> 0}, []}, {token, 308, 310, few, {sentence -> 0}, []}, {token, 312, 316, times, {sentence -> 0}, []}, {token, 317, 317, ,, {sentence -> 0}, []}, {token, 319, 321, you, {sentence -> 0}, []}, {token, 323, 328, really, {sentence -> 0}, []}, {token, 330, 334, start, {sentence -> 0}, []}, {token, 336, 341, liking, {sentence -> 0}, []}, {token, 343, 344, it, {sentence -> 0}, []}, {token, 345, 345, ., {sentence -> 0}, []}] |[{category, 0, 345, pos, {sentence -> 0, Some(neg) -> 8.608082E-4, Some(pos) -> 0.9991392}, []}] |
|This was the worst movie I saw at WorldFest and it also received the least amount of applause afterwards! I can only think it is receiving such recognition based on the amount of known actors in the film. It's great to see J.Beals but she's only in the movie for a few minutes. M.Parker is a much better actress than the part allowed for. The rest of the acting is hard to judge because the movie is so ridiculous and predictable. The main character is totally unsympathetic and therefore a bore to watch. There is no real emotional depth to the story. A movie revolving about an actor who can't get work doesn't feel very original to me. Nor does the development of the cop. It feels like one of many straight-to-video movies I saw back in the 90s ... And not even a good one in those standards.<br /><br /> |negative |[{document, 0, 807, This was the worst movie I saw at WorldFest and it also received the least amount of applause afterwards! 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|The Karen Carpenter Story shows a little more about singer Karen Carpenter's complex life. Though it fails in giving accurate facts, and details.<br /><br />Cynthia Gibb (portrays Karen) was not a fine election. She is a good actress , but plays a very naive and sort of dumb Karen Carpenter. I think that the role needed a stronger character. Someone with a stronger personality.<br /><br />Louise Fletcher role as Agnes Carpenter is terrific, she does a great job as Karen's mother.<br /><br />It has great songs, which could have been included in a soundtrack album. Unfortunately they weren't, though this movie was on the top of the ratings in USA and other several countries |positive |[{document, 0, 679, The Karen Carpenter Story shows a little more about singer Karen Carpenter's complex life. Though it fails in giving accurate facts, and details.<br /><br />Cynthia Gibb (portrays Karen) was not a fine election. She is a good actress , but plays a very naive and sort of dumb Karen Carpenter. I think that the role needed a stronger character. Someone with a stronger personality.<br /><br />Louise Fletcher role as Agnes Carpenter is terrific, she does a great job as Karen's mother.<br /><br />It has great songs, which could have been included in a soundtrack album. 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582, Unfortunately, {sentence -> 0}, []}, {token, 584, 587, they, {sentence -> 0}, []}, {token, 589, 595, weren't, {sentence -> 0}, []}, {token, 596, 596, ,, {sentence -> 0}, []}, {token, 598, 603, though, {sentence -> 0}, []}, {token, 605, 608, this, {sentence -> 0}, []}, {token, 610, 614, movie, {sentence -> 0}, []}, {token, 616, 618, was, {sentence -> 0}, []}, {token, 620, 621, on, {sentence -> 0}, []}, {token, 623, 625, the, {sentence -> 0}, []}, {token, 627, 629, top, {sentence -> 0}, []}, {token, 631, 632, of, {sentence -> 0}, []}, {token, 634, 636, the, {sentence -> 0}, []}, {token, 638, 644, ratings, {sentence -> 0}, []}, {token, 646, 647, in, {sentence -> 0}, []}, {token, 649, 651, USA, {sentence -> 0}, []}, {token, 653, 655, and, {sentence -> 0}, []}, {token, 657, 661, other, {sentence -> 0}, []}, {token, 663, 669, several, {sentence -> 0}, []}, {token, 671, 679, countries, {sentence -> 0}, []}] |[{category, 0, 679, pos, {sentence -> 0, Some(neg) -> 0.17365533, Some(pos) -> 0.82634467}, []}] |
|This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo... the list goes on and on. It failed miserably at all of them, but there was enough interest to keep me from turning it off until the end.<br /><br />Although I appreciate the spirit behind WAR, INC., it depresses me to see such a clumsy effort, especially when it will be taken by its targets to reflect the lack of the existence of a serious critique, rather than simply the poor writing, direction, and production of this particular film.<br /><br />There is a critique to be made about the corporatization of war. But poking fun at it in this way diminishes the true atrocity of what is happening. Reminds me a bit of THREE KINGS, which similarly trivializes a genuine cause for concern. |negative |[{document, 0, 835, This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo... the list goes on and on. It failed miserably at all of them, but there was enough interest to keep me from turning it off until the end.<br /><br />Although I appreciate the spirit behind WAR, INC., it depresses me to see such a clumsy effort, especially when it will be taken by its targets to reflect the lack of the existence of a serious critique, rather than simply the poor writing, direction, and production of this particular film.<br /><br />There is a critique to be made about the corporatization of war. But poking fun at it in this way diminishes the true atrocity of what is happening. 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-> 0}, []}, {token, 767, 771, THREE, {sentence -> 0}, []}, {token, 773, 777, KINGS, {sentence -> 0}, []}, {token, 778, 778, ,, {sentence -> 0}, []}, {token, 780, 784, which, {sentence -> 0}, []}, {token, 786, 794, similarly, {sentence -> 0}, []}, {token, 796, 806, trivializes, {sentence -> 0}, []}, {token, 808, 808, a, {sentence -> 0}, []}, {token, 810, 816, genuine, {sentence -> 0}, []}, {token, 818, 822, cause, {sentence -> 0}, []}, {token, 824, 826, for, {sentence -> 0}, []}, {token, 828, 834, concern, {sentence -> 0}, []}, {token, 835, 835, ., {sentence -> 0}, []}] |[{category, 0, 835, neg, {sentence -> 0, Some(neg) -> 0.99918306, Some(pos) -> 8.1692083E-4}, []}] |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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only showing top 20 rows
In [22]:
# apply trained model to text
example = spark.createDataFrame([[comment]]).toDF("text")
result = pipelineModel.transform(example)
result.select('text', 'class').show(truncate=False)
result.selectExpr('document.result[0] as sentence', 'class.result[0] as sentiment', 'class.metadata as metadata').show(truncate=False)
+--------------------------------------------+------------------------------------------------------------------------------------------------+
|text |class |
+--------------------------------------------+------------------------------------------------------------------------------------------------+
|The movie I watched today was not a good one|[{category, 0, 43, neg, {sentence -> 0, Some(neg) -> 0.9989093, Some(pos) -> 0.0010907185}, []}]|
+--------------------------------------------+------------------------------------------------------------------------------------------------+
+--------------------------------------------+---------+--------------------------------------------------------------------+
|sentence |sentiment|metadata |
+--------------------------------------------+---------+--------------------------------------------------------------------+
|The movie I watched today was not a good one|neg |[{sentence -> 0, Some(neg) -> 0.9989093, Some(pos) -> 0.0010907185}]|
+--------------------------------------------+---------+--------------------------------------------------------------------+
使用 spark-nlp 深度学习模型进行情感分析¶
- spark-nlp 提供的最佳预训练情感分析模型之一。
- 该模型在斯坦福 IMDB 电影评论数据集（25,000 条评论）上训练。
- 支持英文文本的情感分类任务，能够区
In [23]:
# https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/SENTIMENT_EN.ipynb
# https://nlp.johnsnowlabs.com/2021/01/15/sentimentdl_use_imdb_en.html (Trained on Stanford sentimetn 25,000 movies)
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained(name="tfhub_use", lang="en")\
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
# notice SentimentDLModel not ClassifierDLModel
sentimentdl = SentimentDLModel.pretrained(name='sentimentdl_use_imdb', lang="en")\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("sentiment")
nlpPipeline = Pipeline(
stages = [
documentAssembler,
use,
sentimentdl
])
empty_data = spark.createDataFrame([['']]).toDF("text")
pipelineModel = nlpPipeline.fit(empty_data)
tfhub_use download started this may take some time. Approximate size to download 923.7 MB [OK!] sentimentdl_use_imdb download started this may take some time. Approximate size to download 12 MB [OK!]
In [24]:
# apply trained model to text
example = spark.createDataFrame([[comment]]).toDF("text")
result = pipelineModel.transform(example)
result.select('text', 'sentiment').show(truncate=False)
result.selectExpr('document.result[0] as sentence', 'sentiment.result[0] as sentiment', 'sentiment.metadata as metadata').show(truncate=False)
+--------------------------------------------+------------------------------------------------------------------------------------+
|text |sentiment |
+--------------------------------------------+------------------------------------------------------------------------------------+
|The movie I watched today was not a good one|[{category, 0, 43, pos, {sentence -> 0, pos -> 0.9950311, neg -> 0.0049688937}, []}]|
+--------------------------------------------+------------------------------------------------------------------------------------+
+--------------------------------------------+---------+--------------------------------------------------------+
|sentence |sentiment|metadata |
+--------------------------------------------+---------+--------------------------------------------------------+
|The movie I watched today was not a good one|pos |[{sentence -> 0, pos -> 0.9950311, neg -> 0.0049688937}]|
+--------------------------------------------+---------+--------------------------------------------------------+
使用 Bert 情感分类模型进行情绪分析 (bert_sequence_classifier_emotion)¶
- spark-nlp 提供的预训练 Bert 模型可用于英文文本的情绪分类任务。
- 该模型能够识别多种情绪类别，如愤怒、快乐、悲伤等。
- 适用于社交媒体、评论等场景的情绪识别。
In [25]:
# https://nlp.johnsnowlabs.com/2022/01/14/bert_sequence_classifier_emotion_en.html
# https://huggingface.co/nateraw/bert-base-uncased-emotion: "Not the best model, but it works in a pinch I guess..."
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
sequenceClassifier = BertForSequenceClassification \
.pretrained('bert_sequence_classifier_emotion', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class')
pipeline = Pipeline(stages=[document_assembler, tokenizer, sequenceClassifier])
# Rely on the given trained model, so "train" on and empty data to create the model.
empty_data = spark.createDataFrame([['']]).toDF("text")
# Fit to data
pipelineModel = pipeline.fit(empty_data)
bert_sequence_classifier_emotion download started this may take some time. Approximate size to download 391.1 MB [OK!]
In [26]:
# apply trained model to text
example = spark.createDataFrame([[comment]]).toDF("text")
result = pipelineModel.transform(example)
result.selectExpr('document.result[0] as sentence', 'class.result[0] as sentiment', 'class.metadata as metadata').show(truncate=False)
+--------------------------------------------+---------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|sentence |sentiment|metadata |
+--------------------------------------------+---------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|The movie I watched today was not a good one|joy |[{Some(sadness) -> 0.017389426, Some(surprise) -> 0.0030828605, Some(fear) -> 0.003483941, Some(love) -> 0.005832119, Some(anger) -> 0.01538162, Some(joy) -> 0.95483005, sentence -> 0}]|
+--------------------------------------------+---------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
In [27]:
# Similar, but not exactly the same as found with original model:
# https://huggingface.co/nateraw/bert-base-uncased-emotion?text=I+like+you.+I+love+you
example = spark.createDataFrame([["I like you. I love you"]]).toDF("text")
result = pipelineModel.transform(example)
result.selectExpr('document.result[0] as sentence', 'class.result[0] as sentiment', 'class.metadata as metadata').show(truncate=False)
+----------------------+---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|sentence |sentiment|metadata |
+----------------------+---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|I like you. I love you|love |[{Some(sadness) -> 0.027391676, Some(surprise) -> 0.002645207, Some(fear) -> 0.002119432, Some(love) -> 0.61994994, Some(anger) -> 0.015406931, Some(joy) -> 0.3324868, sentence -> 0}]|
+----------------------+---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
使用 spark-nlp 深度学习模型进行情绪分析 (spark-nlp deep learning model)¶
- spark-nlp 提供的最佳预训练情绪分析模型之一。
- 该模型能够识别多种情绪类别，如愤怒、快乐、悲伤等。
- 支持英文文本的情绪分类任务，适用于社交媒体、评论等场景的情绪识别。
In [28]:
# https://nlp.johnsnowlabs.com/2020/07/03/classifierdl_use_emotion_en.html
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained(name="tfhub_use", lang="en")\
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
sentimentdl = ClassifierDLModel.pretrained(name='classifierdl_use_emotion')\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("sentiment")
nlpPipeline = Pipeline(
stages = [
documentAssembler,
use,
sentimentdl
])
empty_data = spark.createDataFrame([['']]).toDF("text")
pipelineModel = nlpPipeline.fit(empty_data)
tfhub_use download started this may take some time. Approximate size to download 923.7 MB [OK!] classifierdl_use_emotion download started this may take some time. Approximate size to download 21.3 MB [OK!]
In [29]:
# apply trained model to text
example = spark.createDataFrame([[comment]]).toDF("text")
result = pipelineModel.transform(example)
result.selectExpr('text', 'sentiment.result as result', 'sentiment.metadata as metadata').show(truncate=False)
+--------------------------------------------+---------+---------------------------------------------------------------------------------------------------------+
|text |result |metadata |
+--------------------------------------------+---------+---------------------------------------------------------------------------------------------------------+
|The movie I watched today was not a good one|[sadness]|[{surprise -> 0.03977137, joy -> 6.099707E-4, fear -> 2.8340122E-5, sadness -> 0.9595903, sentence -> 0}]|
+--------------------------------------------+---------+---------------------------------------------------------------------------------------------------------+
In [30]:
# Compare with https://nlp.johnsnowlabs.com/2020/07/03/classifierdl_use_emotion_en.html
sample = spark.createDataFrame([["@Mira I just saw you on live t.v!!"],
["Just home from group celebration - dinner at Trattoria Gianni, then Hershey Felder's performance - AMAZING!!"],
["Nooooo! My dad turned off the internet so I can't listen to band music!"],
["My soul has just been pierced by the most evil look from @rickosborneorg. A mini panic attack and chill in bones followed soon after."]]).toDF("text")
result = pipelineModel.transform(sample)
result.selectExpr('text', 'sentiment.result as result', 'sentiment.metadata as metadata').show(truncate=False)
+-------------------------------------------------------------------------------------------------------------------------------------+----------+------------------------------------------------------------------------------------------------------------+
|text |result |metadata |
+-------------------------------------------------------------------------------------------------------------------------------------+----------+------------------------------------------------------------------------------------------------------------+
|@Mira I just saw you on live t.v!! |[surprise]|[{surprise -> 0.99995255, joy -> 2.7403673E-6, fear -> 8.520834E-6, sadness -> 3.6288275E-5, sentence -> 0}]|
|Just home from group celebration - dinner at Trattoria Gianni, then Hershey Felder's performance - AMAZING!! |[joy] |[{surprise -> 2.55246E-7, joy -> 0.99999976, fear -> 8.720782E-10, sadness -> 5.25679E-8, sentence -> 0}] |
|Nooooo! My dad turned off the internet so I can't listen to band music! |[sadness] |[{surprise -> 4.5728097E-8, joy -> 6.957345E-8, fear -> 3.4834052E-8, sadness -> 0.9999999, sentence -> 0}] |
|My soul has just been pierced by the most evil look from @rickosborneorg. A mini panic attack and chill in bones followed soon after.|[fear] |[{surprise -> 2.138023E-6, joy -> 8.280158E-5, fear -> 0.9999149, sadness -> 6.917839E-8, sentence -> 0}] |
+-------------------------------------------------------------------------------------------------------------------------------------+----------+------------------------------------------------------------------------------------------------------------+
使用 spark-nlp 深度学习模型进行示例¶
In [31]:
# https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/SENTIMENT_EN_EMOTION.ipynb#scrollTo=1XxHWemdE5hX
MODEL_NAME='classifierdl_use_emotion'
text_list = [
"""I am SO happy the news came out in time for my birthday this weekend! My inner 7-year-old cannot WAIT!""",
"""That moment when you see your friend in a commercial. Hahahaha!""",
"""My soul has just been pierced by the most evil look from @rickosborneorg. A mini panic attack & chill in bones followed soon after.""",
"""For some reason I woke up thinkin it was Friday then I got to school and realized its really Monday -_-""",
"""I'd probably explode into a jillion pieces from the inablility to contain all of my if I had a Whataburger patty melt right now. #drool""",
"""These are not emotions. They are simply irrational thoughts feeding off of an emotion""",
"""Found out im gonna be with sarah bo barah in ny for one day!!! Eggcitement :)""",
"""That awkward moment when you find a perfume box full of sensors!""",
"""Just home from group celebration - dinner at Trattoria Gianni, then Hershey Felder's performance - AMAZING!!""",
"""Nooooo! My dad turned off the internet so I can't listen to band music!""",
]
In [32]:
# Apply the model to the data
import pandas as pd
df = spark.createDataFrame(pd.DataFrame({"text":text_list}))
result = pipelineModel.transform(df)
result.selectExpr('text', 'sentiment.result as result', 'sentiment.metadata as metadata').show(truncate=False)
+---------------------------------------------------------------------------------------------------------------------------------------+----------+-------------------------------------------------------------------------------------------------------------+
|text |result |metadata |
+---------------------------------------------------------------------------------------------------------------------------------------+----------+-------------------------------------------------------------------------------------------------------------+
|I am SO happy the news came out in time for my birthday this weekend! My inner 7-year-old cannot WAIT! |[surprise]|[{surprise -> 0.9964477, joy -> 0.0035500138, fear -> 4.2735905E-7, sadness -> 1.9294253E-6, sentence -> 0}] |
|That moment when you see your friend in a commercial. Hahahaha! |[surprise]|[{surprise -> 0.99999964, joy -> 1.9871182E-7, fear -> 1.5814007E-7, sadness -> 4.1269594E-8, sentence -> 0}]|
|My soul has just been pierced by the most evil look from @rickosborneorg. A mini panic attack & chill in bones followed soon after.|[fear] |[{surprise -> 2.8584633E-4, joy -> 0.015555172, fear -> 0.9841575, sadness -> 1.5160624E-6, sentence -> 0}] |
|For some reason I woke up thinkin it was Friday then I got to school and realized its really Monday -_- |[sadness] |[{surprise -> 0.05638051, joy -> 0.24535467, fear -> 1.1346062E-5, sadness -> 0.6982535, sentence -> 0}] |
|I'd probably explode into a jillion pieces from the inablility to contain all of my if I had a Whataburger patty melt right now. #drool|[sadness] |[{surprise -> 3.0657734E-6, joy -> 1.3458956E-4, fear -> 8.604002E-7, sadness -> 0.9998615, sentence -> 0}] |
|These are not emotions. They are simply irrational thoughts feeding off of an emotion |[fear] |[{surprise -> 1.0121462E-16, joy -> 4.1430238E-13, fear -> 1.0, sadness -> 6.4368524E-11, sentence -> 0}] |
|Found out im gonna be with sarah bo barah in ny for one day!!! Eggcitement :) |[surprise]|[{surprise -> 0.99979454, joy -> 4.838901E-7, fear -> 5.5589997E-8, sadness -> 2.0491533E-4, sentence -> 0}] |
|That awkward moment when you find a perfume box full of sensors! |[surprise]|[{surprise -> 0.9999715, joy -> 2.5127543E-5, fear -> 3.1423615E-6, sadness -> 2.5378586E-7, sentence -> 0}] |
|Just home from group celebration - dinner at Trattoria Gianni, then Hershey Felder's performance - AMAZING!! |[joy] |[{surprise -> 2.5524548E-7, joy -> 0.99999976, fear -> 8.720782E-10, sadness -> 5.2568E-8, sentence -> 0}] |
|Nooooo! My dad turned off the internet so I can't listen to band music! |[sadness] |[{surprise -> 4.572801E-8, joy -> 6.9573325E-8, fear -> 3.4833985E-8, sadness -> 0.9999999, sentence -> 0}] |
+---------------------------------------------------------------------------------------------------------------------------------------+----------+-------------------------------------------------------------------------------------------------------------+
In [33]:
# Display results
from pyspark.sql import functions as F
r = result.selectExpr("document.result as document", "sentiment.result as sentiment") \
.select(F.explode(F.arrays_zip('document', 'sentiment')).alias('cols')).selectExpr("cols.*")
pdf = r.toPandas()
pdf
Out[33]:
| document | sentiment | |
|---|---|---|
| 0 | I am SO happy the news came out in time for my... | surprise |
| 1 | That moment when you see your friend in a comm... | surprise |
| 2 | My soul has just been pierced by the most evil... | fear |
| 3 | For some reason I woke up thinkin it was Frida... | sadness |
| 4 | I'd probably explode into a jillion pieces fro... | sadness |
| 5 | These are not emotions. They are simply irrati... | fear |
| 6 | Found out im gonna be with sarah bo barah in n... | surprise |
| 7 | That awkward moment when you find a perfume bo... | surprise |
| 8 | Just home from group celebration - dinner at T... | joy |
| 9 | Nooooo! My dad turned off the internet so I ca... | sadness |
Question / Answer models¶
只有在运行 T5 相关代码之前，先重启运行环境（如 Jupyter Notebook 的 Kernel），T5 模型才能正常加载和使用。如果不重启，可能会因为依赖或内存等问题导致 T5 代码无法正常运行。
Download T5 Model and Create Spark NLP Pipeline¶
In [34]:
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
# Can take in document or sentence columns
t5 = T5Transformer.pretrained(name='t5_base',lang='en')\
.setInputCols('document')\
.setOutputCol("T5")\
.setMaxOutputLength(400)
t5_base download started this may take some time. Approximate size to download 451.8 MB [OK!]
Set the Task to question¶
In [35]:
# Set the task for questions on T5. Depending to what this is currently set, we get different behaivour
t5.setTask('question')
Out[35]:
T5TRANSFORMER_8078c2d39352
Answer Closed Book Questions¶
闭卷问题指的是不给模型任何额外的上下文信息，模型必须依靠自身权重中存储的知识来回答问题。
In [36]:
# Build pipeline with T5
pipe_components = [documentAssembler,t5]
pipeline = Pipeline().setStages(pipe_components)
# define Data
data = [["Who is president of the USA? "],
["What is the capital of the USA?"],
["What is the capital of Montana?"],
["What is the best breed of dog?"],
["Who is president of Nigeria? "],
["What is the most common language in India? "],
["What is the capital of Germany? "],]
df=spark.createDataFrame(data).toDF('text')
#Predict on text data with T5
model = pipeline.fit(df)
annotated_df = model.transform(df)
annotated_df.select(['text','t5.result']).show(truncate=False)
+-------------------------------------------+------------------+ |text |result | +-------------------------------------------+------------------+ |Who is president of the USA? |[John F. Kennedy] | |What is the capital of the USA? |[Washington] | |What is the capital of Montana? |[Montana] | |What is the best breed of dog? |[dog] | |Who is president of Nigeria? |[Muhammadu Buhari]| |What is the most common language in India? |[Hindi] | |What is the capital of Germany? |[Berlin] | +-------------------------------------------+------------------+
Answer Open Book Questions¶
这些问题会给模型一些额外的上下文信息，模型会利用这些信息来回答问题。
In [37]:
context = 'context: Peters last week was terrible! He had an accident and broke his leg while skiing!'
question1 = 'question: Why was peters week so bad? ' #
question2 = 'question: How did peter broke his leg? '
question3 = 'question: How did peter broke his leg? '
data = [[question1+context],[question2+context],[question3+context],]
df=spark.createDataFrame(data).toDF('text')
#Predict on text data with T5
model = pipeline.fit(df)
annotated_df = model.transform(df)
annotated_df.select(['text','t5.result']).show(truncate=False)
+---------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+ |text |result | +---------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+ |question: Why was peters week so bad? context: Peters last week was terrible! He had an accident and broke his leg while skiing! |[He had an accident and broke his leg while skiing]| |question: How did peter broke his leg? context: Peters last week was terrible! He had an accident and broke his leg while skiing!|[skiing] | |question: How did peter broke his leg? context: Peters last week was terrible! He had an accident and broke his leg while skiing!|[skiing] | +---------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+
In [ ]:
# Ask T5 questions in the context of a News Article
question1 = 'question: Who is Jack ma? '
question2 = 'question: Who is founder of Alibaba Group? '
question3 = 'question: When did Jack Ma re-appear? '
question4 = 'question: How did Alibaba stocks react? '
question5 = 'question: Whom did Jack Ma meet? '
question6 = 'question: Who did Jack Ma hide from? '
# from https://www.bbc.com/news/business-55728338
news_article_context = """ context:
Alibaba Group founder Jack Ma has made his first appearance since Chinese regulators cracked down on his business empire.
His absence had fuelled speculation over his whereabouts amid increasing official scrutiny of his businesses.
The billionaire met 100 rural teachers in China via a video meeting on Wednesday, according to local government media.
Alibaba shares surged 5% on Hong Kong's stock exchange on the news.
"""
data = [
[question1+ news_article_context],
[question2+ news_article_context],
[question3+ news_article_context],
[question4+ news_article_context],
[question5+ news_article_context],
[question6+ news_article_context]]
df=spark.createDataFrame(data).toDF('text')
In [ ]:
#Predict on text data with T5
model = pipeline.fit(df)
annotated_df = model.transform(df)
annotated_df.select(['t5.result']).show(truncate=False)
+-----------------------+ |result | +-----------------------+ |[Alibaba Group founder]| |[Jack Ma] | |[Wednesday] | |[surged 5%] | |[100 rural teachers] | |[Chinese regulators] | +-----------------------+
Summarize documents¶
In [39]:
# Set the task for questions on T5
t5.setTask('summarize')
Out[39]:
T5TRANSFORMER_8078c2d39352
In [ ]:
# https://www.reuters.com/article/instant-article/idCAKBN2AA2WF
text = """(Reuters) - Mastercard Inc said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year, joining a string of big-ticket firms that have pledged similar support.
The credit-card giant’s announcement comes days after Elon Musk’s Tesla Inc revealed it had purchased $1.5 billion of bitcoin and would soon accept it as a form of payment.
Asset manager BlackRock Inc and payments companies Square and PayPal have also recently backed cryptocurrencies.
Mastercard already offers customers cards that allow people to transact using their cryptocurrencies, although without going through its network.
"Doing this work will create a lot more possibilities for shoppers and merchants, allowing them to transact in an entirely new form of payment. This change may open merchants up to new customers who are already flocking to digital assets," Mastercard said. (mstr.cd/3tLaPZM)
Mastercard specified that not all cryptocurrencies will be supported on its network, adding that many of the hundreds of digital assets in circulation still need to tighten their compliance measures.
Many cryptocurrencies have struggled to win the trust of mainstream investors and the general public due to their speculative nature and potential for money laundering.
"""
data = [[text]]
df=spark.createDataFrame(data).toDF('text')
#Predict on text data with T5
model = pipeline.fit(df)
In [ ]:
annotated_df = model.transform(df)
annotated_df.select(['t5.result']).show(truncate=False)
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |result | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |[mastercard said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year . the credit-card giant’s announcement comes days after Elon Musk’s Tesla Inc revealed it had purchased $1.5 billion of bitcoin . asset manager blackrock and payments companies Square and PayPal have also recently backed cryptocurrencies .]| +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
In [ ]:
# 获取摘要结果并比较原文与摘要的长度
v = annotated_df.take(1)
print(f"Original Length {len(v[0].text)} Summarized Length : {len(v[0].T5[0].result)} ")
Original Length 1284 Summarized Length : 352
In [ ]:
# 输出完整的摘要文本
v[0].T5[0].result
Out[ ]:
'mastercard said on Wednesday it was planning to offer support for some cryptocurrencies on its network this year . the credit-card giant’s announcement comes days after Elon Musk’s Tesla Inc revealed it had purchased $1.5 billion of bitcoin . asset manager blackrock and payments companies Square and PayPal have also recently backed cryptocurrencies .'
每个 T5 任务及说明：¶
| 任务名称 | 说明 |
|---|---|
| 1.CoLA | 判断句子语法是否正确 |
| 2.RTE | 判断某个陈述能否从句子中推断出来 |
| 3.MNLI | 判断假设与前提是否矛盾、蕴含或无关（三分类） |
| 4.MRPC | 判断两句话是否为同义改写（语义等价） |
| 5.QNLI | 判断问题的答案能否从候选答案中推断出来 |
| 6.QQP | 判断两组问题是否为同义改写（语义等价） |
| 7.SST2 | 判断句子的情感为正面或负面 |
| 8.STSB | 按 1 到 5 的分值对句子情感进行分类（21 类） |
| 9.CB | 判断前提与假设是否矛盾（二分类） |
| 10.COPA | 针对问题、前提和两个选项，判断哪个选项正确（二分类） |
| 11.MultiRc | 针对问题、文本段落和答案候选，判断答案是否正确（二分类） |
| 12.WiC | 针对两个句子和一个歧义词，判断该词在两句中的含义是否相同 |
| 13.WSC/DPR | 预测句子中模糊代词所指代的内容 |
| 14.Summarization | 将文本摘要为更短的表达 |
| 15.SQuAD | 针对给定上下文回答问题 |
| 16.WMT1. | 英译德 |
| 17.WMT2. | 英译法 |
| 18.WMT3. | 英译罗马尼亚语 |