What this handout is about.
The first step in any successful college writing venture is reading the assignment. While this sounds like a simple task, it can be a tough one. This handout will help you unravel your assignment and begin to craft an effective response. Much of the following advice will involve translating typical assignment terms and practices into meaningful clues to the type of writing your instructor expects. See our short video for more tips.
Regardless of the assignment, department, or instructor, adopting these two habits will serve you well :
- Read the assignment carefully as soon as you receive it. Do not put this task off—reading the assignment at the beginning will save you time, stress, and problems later. An assignment can look pretty straightforward at first, particularly if the instructor has provided lots of information. That does not mean it will not take time and effort to complete; you may even have to learn a new skill to complete the assignment.
- Ask the instructor about anything you do not understand. Do not hesitate to approach your instructor. Instructors would prefer to set you straight before you hand the paper in. That’s also when you will find their feedback most useful.
Many assignments follow a basic format. Assignments often begin with an overview of the topic, include a central verb or verbs that describe the task, and offer some additional suggestions, questions, or prompts to get you started.
An Overview of Some Kind
The instructor might set the stage with some general discussion of the subject of the assignment, introduce the topic, or remind you of something pertinent that you have discussed in class. For example:
“Throughout history, gerbils have played a key role in politics,” or “In the last few weeks of class, we have focused on the evening wear of the housefly …”
The Task of the Assignment
Pay attention; this part tells you what to do when you write the paper. Look for the key verb or verbs in the sentence. Words like analyze, summarize, or compare direct you to think about your topic in a certain way. Also pay attention to words such as how, what, when, where, and why; these words guide your attention toward specific information. (See the section in this handout titled “Key Terms” for more information.)
“Analyze the effect that gerbils had on the Russian Revolution”, or “Suggest an interpretation of housefly undergarments that differs from Darwin’s.”
Additional Material to Think about
Here you will find some questions to use as springboards as you begin to think about the topic. Instructors usually include these questions as suggestions rather than requirements. Do not feel compelled to answer every question unless the instructor asks you to do so. Pay attention to the order of the questions. Sometimes they suggest the thinking process your instructor imagines you will need to follow to begin thinking about the topic.
“You may wish to consider the differing views held by Communist gerbils vs. Monarchist gerbils, or Can there be such a thing as ‘the housefly garment industry’ or is it just a home-based craft?”
These are the instructor’s comments about writing expectations:
“Be concise”, “Write effectively”, or “Argue furiously.”
These instructions usually indicate format rules or guidelines.
“Your paper must be typed in Palatino font on gray paper and must not exceed 600 pages. It is due on the anniversary of Mao Tse-tung’s death.”
The assignment’s parts may not appear in exactly this order, and each part may be very long or really short. Nonetheless, being aware of this standard pattern can help you understand what your instructor wants you to do.
Interpreting the assignment
Ask yourself a few basic questions as you read and jot down the answers on the assignment sheet:
Why did your instructor ask you to do this particular task?
Who is your audience.
- What kind of evidence do you need to support your ideas?
What kind of writing style is acceptable?
- What are the absolute rules of the paper?
Try to look at the question from the point of view of the instructor. Recognize that your instructor has a reason for giving you this assignment and for giving it to you at a particular point in the semester. In every assignment, the instructor has a challenge for you. This challenge could be anything from demonstrating an ability to think clearly to demonstrating an ability to use the library. See the assignment not as a vague suggestion of what to do but as an opportunity to show that you can handle the course material as directed. Paper assignments give you more than a topic to discuss—they ask you to do something with the topic. Keep reminding yourself of that. Be careful to avoid the other extreme as well: do not read more into the assignment than what is there.
Of course, your instructor has given you an assignment so that he or she will be able to assess your understanding of the course material and give you an appropriate grade. But there is more to it than that. Your instructor has tried to design a learning experience of some kind. Your instructor wants you to think about something in a particular way for a particular reason. If you read the course description at the beginning of your syllabus, review the assigned readings, and consider the assignment itself, you may begin to see the plan, purpose, or approach to the subject matter that your instructor has created for you. If you still aren’t sure of the assignment’s goals, try asking the instructor. For help with this, see our handout on getting feedback .
Given your instructor’s efforts, it helps to answer the question: What is my purpose in completing this assignment? Is it to gather research from a variety of outside sources and present a coherent picture? Is it to take material I have been learning in class and apply it to a new situation? Is it to prove a point one way or another? Key words from the assignment can help you figure this out. Look for key terms in the form of active verbs that tell you what to do.
Key Terms: Finding Those Active Verbs
Here are some common key words and definitions to help you think about assignment terms:
Information words Ask you to demonstrate what you know about the subject, such as who, what, when, where, how, and why.
- define —give the subject’s meaning (according to someone or something). Sometimes you have to give more than one view on the subject’s meaning
- describe —provide details about the subject by answering question words (such as who, what, when, where, how, and why); you might also give details related to the five senses (what you see, hear, feel, taste, and smell)
- explain —give reasons why or examples of how something happened
- illustrate —give descriptive examples of the subject and show how each is connected with the subject
- summarize —briefly list the important ideas you learned about the subject
- trace —outline how something has changed or developed from an earlier time to its current form
- research —gather material from outside sources about the subject, often with the implication or requirement that you will analyze what you have found
Relation words Ask you to demonstrate how things are connected.
- compare —show how two or more things are similar (and, sometimes, different)
- contrast —show how two or more things are dissimilar
- apply—use details that you’ve been given to demonstrate how an idea, theory, or concept works in a particular situation
- cause —show how one event or series of events made something else happen
- relate —show or describe the connections between things
Interpretation words Ask you to defend ideas of your own about the subject. Do not see these words as requesting opinion alone (unless the assignment specifically says so), but as requiring opinion that is supported by concrete evidence. Remember examples, principles, definitions, or concepts from class or research and use them in your interpretation.
- assess —summarize your opinion of the subject and measure it against something
- prove, justify —give reasons or examples to demonstrate how or why something is the truth
- evaluate, respond —state your opinion of the subject as good, bad, or some combination of the two, with examples and reasons
- support —give reasons or evidence for something you believe (be sure to state clearly what it is that you believe)
- synthesize —put two or more things together that have not been put together in class or in your readings before; do not just summarize one and then the other and say that they are similar or different—you must provide a reason for putting them together that runs all the way through the paper
- analyze —determine how individual parts create or relate to the whole, figure out how something works, what it might mean, or why it is important
- argue —take a side and defend it with evidence against the other side
More Clues to Your Purpose As you read the assignment, think about what the teacher does in class:
- What kinds of textbooks or coursepack did your instructor choose for the course—ones that provide background information, explain theories or perspectives, or argue a point of view?
- In lecture, does your instructor ask your opinion, try to prove her point of view, or use keywords that show up again in the assignment?
- What kinds of assignments are typical in this discipline? Social science classes often expect more research. Humanities classes thrive on interpretation and analysis.
- How do the assignments, readings, and lectures work together in the course? Instructors spend time designing courses, sometimes even arguing with their peers about the most effective course materials. Figuring out the overall design to the course will help you understand what each assignment is meant to achieve.
Now, what about your reader? Most undergraduates think of their audience as the instructor. True, your instructor is a good person to keep in mind as you write. But for the purposes of a good paper, think of your audience as someone like your roommate: smart enough to understand a clear, logical argument, but not someone who already knows exactly what is going on in your particular paper. Remember, even if the instructor knows everything there is to know about your paper topic, he or she still has to read your paper and assess your understanding. In other words, teach the material to your reader.
Aiming a paper at your audience happens in two ways: you make decisions about the tone and the level of information you want to convey.
- Tone means the “voice” of your paper. Should you be chatty, formal, or objective? Usually you will find some happy medium—you do not want to alienate your reader by sounding condescending or superior, but you do not want to, um, like, totally wig on the man, you know? Eschew ostentatious erudition: some students think the way to sound academic is to use big words. Be careful—you can sound ridiculous, especially if you use the wrong big words.
- The level of information you use depends on who you think your audience is. If you imagine your audience as your instructor and she already knows everything you have to say, you may find yourself leaving out key information that can cause your argument to be unconvincing and illogical. But you do not have to explain every single word or issue. If you are telling your roommate what happened on your favorite science fiction TV show last night, you do not say, “First a dark-haired white man of average height, wearing a suit and carrying a flashlight, walked into the room. Then a purple alien with fifteen arms and at least three eyes turned around. Then the man smiled slightly. In the background, you could hear a clock ticking. The room was fairly dark and had at least two windows that I saw.” You also do not say, “This guy found some aliens. The end.” Find some balance of useful details that support your main point.
You’ll find a much more detailed discussion of these concepts in our handout on audience .
The Grim Truth
With a few exceptions (including some lab and ethnography reports), you are probably being asked to make an argument. You must convince your audience. It is easy to forget this aim when you are researching and writing; as you become involved in your subject matter, you may become enmeshed in the details and focus on learning or simply telling the information you have found. You need to do more than just repeat what you have read. Your writing should have a point, and you should be able to say it in a sentence. Sometimes instructors call this sentence a “thesis” or a “claim.”
So, if your instructor tells you to write about some aspect of oral hygiene, you do not want to just list: “First, you brush your teeth with a soft brush and some peanut butter. Then, you floss with unwaxed, bologna-flavored string. Finally, gargle with bourbon.” Instead, you could say, “Of all the oral cleaning methods, sandblasting removes the most plaque. Therefore it should be recommended by the American Dental Association.” Or, “From an aesthetic perspective, moldy teeth can be quite charming. However, their joys are short-lived.”
Convincing the reader of your argument is the goal of academic writing. It doesn’t have to say “argument” anywhere in the assignment for you to need one. Look at the assignment and think about what kind of argument you could make about it instead of just seeing it as a checklist of information you have to present. For help with understanding the role of argument in academic writing, see our handout on argument .
What kind of evidence do you need?
There are many kinds of evidence, and what type of evidence will work for your assignment can depend on several factors–the discipline, the parameters of the assignment, and your instructor’s preference. Should you use statistics? Historical examples? Do you need to conduct your own experiment? Can you rely on personal experience? See our handout on evidence for suggestions on how to use evidence appropriately.
Make sure you are clear about this part of the assignment, because your use of evidence will be crucial in writing a successful paper. You are not just learning how to argue; you are learning how to argue with specific types of materials and ideas. Ask your instructor what counts as acceptable evidence. You can also ask a librarian for help. No matter what kind of evidence you use, be sure to cite it correctly—see the UNC Libraries citation tutorial .
You cannot always tell from the assignment just what sort of writing style your instructor expects. The instructor may be really laid back in class but still expect you to sound formal in writing. Or the instructor may be fairly formal in class and ask you to write a reflection paper where you need to use “I” and speak from your own experience.
Try to avoid false associations of a particular field with a style (“art historians like wacky creativity,” or “political scientists are boring and just give facts”) and look instead to the types of readings you have been given in class. No one expects you to write like Plato—just use the readings as a guide for what is standard or preferable to your instructor. When in doubt, ask your instructor about the level of formality she or he expects.
No matter what field you are writing for or what facts you are including, if you do not write so that your reader can understand your main idea, you have wasted your time. So make clarity your main goal. For specific help with style, see our handout on style .
Technical details about the assignment
The technical information you are given in an assignment always seems like the easy part. This section can actually give you lots of little hints about approaching the task. Find out if elements such as page length and citation format (see the UNC Libraries citation tutorial ) are negotiable. Some professors do not have strong preferences as long as you are consistent and fully answer the assignment. Some professors are very specific and will deduct big points for deviations.
Usually, the page length tells you something important: The instructor thinks the size of the paper is appropriate to the assignment’s parameters. In plain English, your instructor is telling you how many pages it should take for you to answer the question as fully as you are expected to. So if an assignment is two pages long, you cannot pad your paper with examples or reword your main idea several times. Hit your one point early, defend it with the clearest example, and finish quickly. If an assignment is ten pages long, you can be more complex in your main points and examples—and if you can only produce five pages for that assignment, you need to see someone for help—as soon as possible.
Tricks that don’t work
Your instructors are not fooled when you:
- spend more time on the cover page than the essay —graphics, cool binders, and cute titles are no replacement for a well-written paper.
- use huge fonts, wide margins, or extra spacing to pad the page length —these tricks are immediately obvious to the eye. Most instructors use the same word processor you do. They know what’s possible. Such tactics are especially damning when the instructor has a stack of 60 papers to grade and yours is the only one that low-flying airplane pilots could read.
- use a paper from another class that covered “sort of similar” material . Again, the instructor has a particular task for you to fulfill in the assignment that usually relates to course material and lectures. Your other paper may not cover this material, and turning in the same paper for more than one course may constitute an Honor Code violation . Ask the instructor—it can’t hurt.
- get all wacky and “creative” before you answer the question . Showing that you are able to think beyond the boundaries of a simple assignment can be good, but you must do what the assignment calls for first. Again, check with your instructor. A humorous tone can be refreshing for someone grading a stack of papers, but it will not get you a good grade if you have not fulfilled the task.
Critical reading of assignments leads to skills in other types of reading and writing. If you get good at figuring out what the real goals of assignments are, you are going to be better at understanding the goals of all of your classes and fields of study.
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Assignment 3.3 | Week-5 | Programming for Everybody (Getting Started with Python) By Coursera
Coursera Programming for Everybody (Getting Started with Python) Week 5 Assignment 3.3
Question: 3.3 Write a program to prompt for a score between 0.0 and 1.0. If the score is out of range, print an error. If the score is between 0.0 and 1.0, print a grade using the following table:
>= 0.9 A
>= 0.8 B
>= 0.7 C
>= 0.6 D
If the user enters a value out of range, print a suitable error message and exit. For the test, enter a score of 0.85.
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STEP Support - Assignment 3
This is the third of the 25 Foundation modules. We suggest working through the first assignment and second assignment before starting this.
STEP is a challenging examination, and is different in style from A-level, although the mathematical content is the same. STEP questions are longer: they are designed to take around 30 minutes, rather than the typical 10 minutes required for an A-level question.
Do not worry if the STEP problems appear difficult: they are meant to be! However, you should not be daunted. These assignments are designed to help you to develop the skills you need, over time, so that by the time you sit the STEP exam in the summer of Y13 you will feel well-prepared.
About this assignment
The assignment is published as a pdf file below. Each STEP Support assignment module starts with a warm-up exercise, followed by preparatory work leading to a STEP question. Finally, there is a warm-down exercise.
The warm up for this assignment involves the sigma notation, and a proof of the formula for the sum of the terms of a geometric progression. For the last part, use the formula rather than summing the individual terms, and try to do this without using a calculator.
The main STEP question (2004 STEP 1 Question 2) introduces the “floor” notation. More information on this and related functions can be found here .
The final question involves a linear Diophantine equation, i.e. one of the form ax+by = c where a, b and c are are given integers and we are looking for a solution where x and y are also integers.
More information on Diophantine equations can be found in this article on Plus, the free online mathematics magazine, and in this Wikipedia entry . You may also enjoy watching this talk by Dr Vicky Neale - 'How to Solve Equations' This Plus article shows how we can integrate from first principles. This Numberphile video discusses the "Monkeys and Coconuts" problem. Do not watch it until you have tried the assignment!
Hints, support and self evaluation
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Here is a Worked Video Solution to the STEP question from this assignment.
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MGMT 4190/6560 Introduction to Machine Learning Applications @Rensselaer
Assignment 3 ¶
Save your working file in Google drive so that all changes will be saved as you work. MAKE SURE that your final version is saved to GitHub.
Before you turn this in, make sure everything runs as expected. First, restart the kernel (in the menu, select Kernel → Restart) and then run all cells (in the menubar, select Cell → Run All). You can speak with others regarding the assignment but all work must be your own.
This is a 30 point assignment. ¶
You may find it useful to go through the notebooks from the course materials when doing these exercises.
If you attempt to fake passing the tests you will receive a 0 on the assignment and it will be considered an ethical violation.
Exercises - For and If and Functions ¶
(1). Create a function list_step that accepts 3 variables ( start , stop , step ). The function returns a list starting at start , ending at stop , and with a step of step .
list_step(5, 19, 2)
[5, 7, 9, 11, 13, 15, 17]
(2). Create a function list_divisible that accepts 3 variables ( start , stop , divisible ). Use a for loop to create a list of all numbers from start to stop which are divisible by divisible .
list_divisible(1, 50, 7)
[7, 14, 21, 28, 35, 42, 49]
(3). Create a function list_divisible_not that accepts 4 variables ( start , stop , divisible , not_divisible ). Use a for loop to create a list of all numbers from start to stop which are divisible by divisible but not divisible by not_divisible .
list_divisible_not(1, 100, 4, 3)
[4, 8, 16, 20, 28, 32, 40, 44, 52, 56, 64, 68, 76, 80, 88, 92]
The following exercises will use the titanic data from Kaggle. I’ve included it in the input folder just like Kaggle.
(4) What is the key difference between the train and the test?
(5) Create a new column family that is equal to the SibSp * Parch for both the train and the test dataframes. DON’T use a for loop.
(6). While we can submit our answer to Kaggle to see how it will perform, we can also utilize our training data to assess accuracy. Accuracy is the percentage of predictions made correctly-i.e., the percentage of people in which our prediction regarding their survival is correct. In other words, accuracy = (#correct predictions)/(Total # of predictions). Create a function generate_accuracy which accepts two Pandas series objects ( predicted , actual ) and returns the accuracy.
For example, when a and b are two different Pandas Series: generate_accuracy(predicted, actual)
For the sample data below, the data should retun 50.0 (i.e., a percentage).
(7) Create a column PredEveryoneDies which is equal to 0 for everyone in both training and testing datasets.
(8) Find the accuracy of PredEveryoneDies in predicting Survived using the function generate_accuracy that you created earlier and assign it to the AccEveryoneDies variable.
(9) In both the training and testing datasets, create the column PredGender that is 1 – if the person is a woman and 0 – if the person is a man. (This is based on the “women and children first” law of shipwrecks). Then set AccGender to the accuracy of the PredGender in the Train dataset.
(10). Create a generate_submission function that accepts a DataFrame, a target column, and a filename ( df , target , filename ) and writes out the submission file with just the passengerID and the Survived columns, where the Survived column is equal to the target column.
For example: submitdie = generate_submission(train, 'PredEveryoneDies', 'submiteveryonedies.csv')
Should return a dataframe with just passengerID and the Survived column.
Make sure your submission file prediction for Survived is an integer and not at float. If you submit a float it may not work.
(11). To use the women and children first protocol, we will need to use the age field. This has some missing values. We are going to replace null values in the train and test set with the median value for each.
For this particular question:
Set the variables AgeMissingTrain and AgeMissingTest using the count of the number of missing values in the train and test sets, respectively.
Set the variable AgeMedianTrain and AgeMedianTest using the median age of the train and test sets, respectively.
(12) For rows in which the age value is missing, set the age to the appropriate median value for the train/test set.
(13). In our initial calculation of the PredGender column, we made our prediction based on whether the individual was male or female. In accordance to the women and children first protocol, we hypothesize that our model could be improved by including whether the individual was a child in addition to gender. We also have a question, what age to use to determine “child”? (People weren’t likely to check for IDs.) We will check 2 ages…<13 and <18 (somewhat arbitrary but have to start somewhere) and see which yields a better accuracy.
Specifically, create 2 predictions as follows:
train['PredGenderAge13'] should be the prediction incorporating both Gender (women survive) and Age (Children Age<13 survived while Age>=13 died) train['PredGenderAge18'] should be the prediction incorporating both Gender (women survive) and Age (Children Age<18 survived while Age>=18 died)
The analysis assumes that you have addressed missing values in the earlier step and you should do it for both the train and test dataframes
(14). Calculate the accuracy for your new predictions. Use PredGenderAge13 in the training set to calculate AccGenderAge13 (you can use your function again!) and PredGenderAge18 to calcuate AccGenderAge18 .
(15). You should find that the accuracy is higher when using 13 as a designation for a child than 18. What does this tell you about the role of age in surviving a shipwreck?
(16) Create a prediction file for the “women and children first” model in using the test dataset and upload it to Kaggle. Go through the process of uploading it to Kaggle. Put your Kaggle username so we can verify your prediction occued.
Make sure your submission file prediction is an integer and not at float. If you submit a float it may not work.
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The assignment-autotest directory contains scripts useful for automated testing Use
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Note that the unit tests will fail on this repository, since assignments are not yet implemented. That's your job :)
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Assignment 3 - Named Entity Recognition (NER)
Assignment 3 - named entity recognition (ner) #.
Welcome to the third programming assignment of Course 3. In this assignment, you will learn to build more complicated models with Trax. By completing this assignment, you will be able to:
Design the architecture of a neural network, train it, and test it.
Process features and represents them
Understand word padding
Test with your own sentence
Important Note on Submission to the AutoGrader #
Before submitting your assignment to the AutoGrader, please make sure you are not doing the following:
You have not added any extra print statement(s) in the assignment.
You have not added any extra code cell(s) in the assignment.
You have not changed any of the function parameters.
You are not using any global variables inside your graded exercises. Unless specifically instructed to do so, please refrain from it and use the local variables instead.
You are not changing the assignment code where it is not required, like creating extra variables.
If you do any of the following, you will get something like, Grader not found (or similarly unexpected) error upon submitting your assignment. Before asking for help/debugging the errors in your assignment, check for these first. If this is the case, and you don’t remember the changes you have made, you can get a fresh copy of the assignment by following these instructions .
Part 1: Exploring the data
1.1 Importing the Data
1.2 Data generator
Part 2: Building the model
Part 3: Train the Model
Part 4: Compute Accuracy
Part 5: Testing with your own sentence
We first start by defining named entity recognition (NER). NER is a subtask of information extraction that locates and classifies named entities in a text. The named entities could be organizations, persons, locations, times, etc.
Is labeled as follows:
French: geopolitical entity
Morocco: geographic entity
Christmas: time indicator
Everything else that is labeled with an O is not considered to be a named entity. In this assignment, you will train a named entity recognition system that could be trained in a few seconds (on a GPU) and will get around 75% accuracy. Then, you will load in the exact version of your model, which was trained for a longer period of time. You could then evaluate the trained version of your model to get 96% accuracy! Finally, you will be able to test your named entity recognition system with your own sentence.
Part 1: Exploring the data #
We will be using a dataset from Kaggle, which we will preprocess for you. The original data consists of four columns: the sentence number, the word, the part of speech of the word, and the tags. A few tags you might expect to see are:
geo: geographical entity
gpe: geopolitical entity
tim: time indicator
nat: natural phenomenon
O: filler word
1.1 Importing the Data #
In this part, we will import the preprocessed data and explore it.
vocab is a dictionary that translates a word string to a unique number. Given a sentence, you can represent it as an array of numbers translating with this dictionary. The dictionary contains a <PAD> token.
When training an LSTM using batches, all your input sentences must be the same size. To accomplish this, you set the length of your sentences to a certain number and add the generic <PAD> token to fill all the empty spaces.
The tag_map is a dictionary that maps the tags that you could have to numbers. Run the cell below to see the possible classes you will be predicting. The prepositions in the tags mean:
I: Token is inside an entity.
B: Token begins an entity.
If you had the sentence
“Sharon flew to Miami on Friday”
The tags would look like:
where you would have three tokens beginning with B-, since there are no multi-token entities in the sequence. But if you added Sharon’s last name to the sentence:
“Sharon Floyd flew to Miami on Friday”
your tags would change to show first “Sharon” as B-per, and “Floyd” as I-per, where I- indicates an inner token in a multi-token sequence.
So you can see that we have already encoded each sentence into a tensor by converting it into a number. We also have 16 possible tags (excluding the ‘0’ tag), as shown in the tag map.
1.2 Data generator #
In python, a generator is a function that behaves like an iterator. It returns the next item in a pre-defined sequence. Here is a link to review python generators.
In many AI applications it is very useful to have a data generator. You will now implement a data generator for our NER application.
Exercise 01 #
Instructions: Implement a data generator function that takes in batch_size, x, y, pad, shuffle where \(x\) is a large list of sentences, and \(y\) is a list of the tags associated with those sentences and pad is a pad value. Return a subset of those inputs in a tuple of two arrays (X,Y) .
X and Y are arrays of dimension ( batch_size, max_len ), where max_len is the length of the longest sentence in that batch . You will pad the X and Y examples with the pad argument. If shuffle=True , the data will be traversed in a random order.
Use this code as an outer loop
so your data generator runs continuously. Within that loop, define two for loops:
The first stores temporal lists of the data samples to be included in the batch, and finds the maximum length of the sentences contained in it.
The second one moves the elements from the temporal list into NumPy arrays pre-filled with pad values.
There are three features useful for defining this generator:
The NumPy full function to fill the NumPy arrays with a pad value. See full function documentation .
Tracking the current location in the incoming lists of sentences. Generators variables hold their values between invocations, so we create an index variable, initialize to zero, and increment by one for each sample included in a batch. However, we do not use the index to access the positions of the list of sentences directly. Instead, we use it to select one index from a list of indexes. In this way, we can change the order in which we traverse our original list, keeping untouched our original list.
Since batch_size and the length of the input lists are not aligned, gathering a batch_size group of inputs may involve wrapping back to the beginning of the input loop. In our approach, it is just enough to reset the index to 0. We can re-shuffle the list of indexes to produce different batches each time.
Part 2: Building the model #
You will now implement the model that will be able to determining the tags of sentences like the following:
The model architecture will be as follows:
Concretely, your inputs will be sentences represented as tensors that are fed to a model with:
An Embedding layer,
A LSTM layer
A Dense layer
A log softmax layer.
Good news! We won’t make you implement the LSTM cell drawn above. You will be in charge of the overall architecture of the model.
Exercise 02 #
Instructions: Implement the initialization step and the forward function of your Named Entity Recognition system. Please utilize help function e.g. help(tl.Dense) for more information on a layer
tl.Serial : Combinator that applies layers serially (by function composition).
You can pass in the layers as arguments to Serial , separated by commas.
For example: tl.Serial(tl.Embeddings(...), tl.Mean(...), tl.Dense(...), tl.LogSoftmax(...))
tl.Embedding : Initializes the embedding. In this case it is the dimension of the model by the size of the vocabulary.
tl.Embedding(vocab_size, d_feature) .
vocab_size is the number of unique words in the given vocabulary.
d_feature is the number of elements in the word embedding (some choices for a word embedding size range from 150 to 300, for example).
tl.LSTM : Trax LSTM layer.
LSTM(n_units) Builds an LSTM layer with hidden state and cell sizes equal to n_units . In trax, n_units should be equal to the size of the embeddings d_feature .
tl.Dense : A dense layer.
tl.Dense(n_units) : The parameter n_units is the number of units chosen for this dense layer.
tl.LogSoftmax : Log of the output probabilities.
Here, you don’t need to set any parameters for LogSoftMax() .
Part 3: Train the Model #
This section will train your model.
Before you start, you need to create the data generators for training and validation data. It is important that you mask padding in the loss weights of your data, which can be done using the id_to_mask argument of trax.data.inputs.add_loss_weights .
3.1 Training the model #
You will now write a function that takes in your model and trains it.
As you’ve seen in the previous assignments, you will first create the TrainTask and EvalTask using your data generator. Then you will use the training.Loop to train your model.
Exercise 03 #
Instructions: Implement the train_model program below to train the neural network above. Here is a list of things you should do:
Create the trainer object by calling trax.supervised.training.Loop and pass in the following:
model = NER
training task that uses the train data generator defined in the cell above
loss_layer = tl.CrossEntropyLoss()
optimizer = trax.optimizers.Adam(0.01)
evaluation task that uses the validation data generator defined in the cell above and the following arguments
metrics for EvalTask : tl.CrossEntropyLoss() and tl.Accuracy()
in EvalTask set n_eval_batches=10 for better evaluation accuracy
output_dir = output_dir
You’ll be using a cross entropy loss , with an Adam optimizer . Please read the trax documentation to get a full understanding. The trax GitHub also contains some useful information and a link to a colab notebook.
On your local machine, you can run this training for 1000 train_steps and get your own model. This training takes about 5 to 10 minutes to run.
Expected output (Approximately)
This value may change between executions, but it must be around 90% of accuracy on train and validations sets, after 100 training steps.
We have trained the model longer, and we give you such a trained model. In that way, we ensure you can continue with the rest of the assignment even if you had some troubles up to here, and also we are sure that everybody will get the same outputs for the last example. However, you are free to try your model, as well.
Part 4: Compute Accuracy #
You will now evaluate in the test set. Previously, you have seen the accuracy on the training set and the validation (noted as eval) set. You will now evaluate on your test set. To get a good evaluation, you will need to create a mask to avoid counting the padding tokens when computing the accuracy.
Exercise 04 #
Instructions: Write a program that takes in your model and uses it to evaluate on the test set. You should be able to get an accuracy of 95%.
Step 1 : model(sentences) will give you the predicted output.
Step 2 : Prediction will produce an output with an added dimension. For each sentence, for each word, there will be a vector of probabilities for each tag type. For each sentence,word, you need to pick the maximum valued tag. This will require np.argmax and careful use of the axis argument.
Step 3 : Create a mask to prevent counting pad characters. It has the same dimension as output. An example below on matrix comparison provides a hint.
Step 4 : Compute the accuracy metric by comparing your outputs against your test labels. Take the sum of that and divide by the total number of unpadded tokens. Use your mask value to mask the padded tokens. Return the accuracy.
Note that the model’s prediction has 3 axes:
the number of examples
the number of words in each example (padded to be as long as the longest sentence in the batch)
the number of possible targets (the 17 named entity tags).
Part 5: Testing with your own sentence #
Below, you can test it out with your own sentence!
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Assignments For Class 3
Assignments for Class 3 have been developed for Standard 3 students based on the latest syllabus and textbooks applicable in CBSE, NCERT and KVS schools. Parents and students can download the full collection of assignments for class 3rd from our website as we have provided all topic-wise assignment free in PDF format which can be downloaded easily. Students are recommended to do these assignments daily by taking printouts and going through the questions and answers for Grade 3. You should try to do these test assignments on a daily basis so that you are able to understand the concepts and details of each chapter in your book and get good marks in class 3rd exams.
Some important features of our free printable Assignments for Class 3 All Subjects
ALPHABET ASSIGNMENTS – Help Students in improving the understanding of alphabets such as upper and lowercase letter recognition, letter tracing and handwriting, Missing letters alphabet, learning color words.
ENGLISH AND LANGUAGE ARTS ASSIGNMENTS – This section includes phonics and CVC words activities. Spring beginning sounds, ending sounds, beginning sounds cut and paste Assignments, CVC word search for kindergarten, vowel practice sheets, word match Assignments and missing vowel Assignments for kindergarten. You will also find Dolch sight words for kindergarten – all 52 words.
MATH ASSIGNMENTS PDF – These Assignments include learning with ten frames, addition and subtraction, place value and fact families, geometry, size comparison, and more. Greater than less than Assignments, making 5 math Assignments, Sorting pumpkins and fall leaves by size, practice the fact families up to 10, Length comparison cut & paste activity.
SHAPES ASSIGNMENTS – Shape book to learn and recognize eight basic shapes with a fun tracing and coloring book, Spot and dot shapes, Reindeer shapes Assignments which helps in shapes recognition practice, rainbow shapes color by number, 2D shapes 3D shapes Assignments, children will sort 2d and 3d shapes, as well as work on 3d shapes characteristics and tracing shapes worksheet.
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TELLING TIME ASSIGNMENTS – Telling time to the hour Assignments – learn to read and write time with analog and digital clocks, quarter past, quarter to and half past – more advanced telling time Assignments!
PATTERNS ASSIGNMENTS – ABC pattern Assignments – fun Assignments for teaching patterns, draw and color pictures to make patterns, enjoy the sweet pictures while making patterns, Do-A-Dot Marker patterns – use the bingo daubers to copy and make patterns and colorful birthday patterns full of balloons, presents, and treats.
COUNTING ASSIGNMENTS – Teach counting to 10 and 20, Counting backward Assignments – count from 100, 50, 20, and 10 all the way to number 1, Count and match Assignments 1-20 – count the pictures and match them with the correct number, Counting to 10 number Assignments – numbers 1 to 10 Assignments packed with learning, Skip counting snails Assignments – enjoy this spring printable while skip counting by numbers all the way up to 10, Counting beads: Skip counting Assignments for kindergarten – learn to skip count with cute beads and Counting backward from 100 to1
COUNTING WITH TEN FRAMES ASSIGNMENTS – Snowflake ten frames counting – count the snowflakes, write the number, and color the ten frames, fall counting Assignments with five and ten frames – ten frame practices for the fall, counting stars: Ten frame Assignments for kindergarten – a fun ten frame practice activity, Blank ten frame templates – get these free templates for use with stickers or manipulatives
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WRITING AND FINE MOTOR SKILLS ASSIGNMENTS – Help your students or kids with their fine motor skills with these tracing, writing, and cutting Assignments. Letter tracing: Aa, Bb, Cc, Dd, Ee, Ff, Gg, Ss, Tracing lowercase letters, Copy the letters Assignments, Days and months tracing Assignments, Fruit and vegetable coloring and tracing pages, back to school tracing sheets and free printable lined paper for handwriting practice
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Assignments For Class 10 Mathematics Probability
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Couch: 3 quick takes on Jonathan Smith being hired as Michigan State's head football coach
1. Jonathan Smith brings to MSU the pedigree of a coach who knows how to build a program
Michigan State had to decide what it wanted to be as a football program, before it decided who to hire, I wrote in October .
We have an answer.
MSU is hiring Oregon State’s Jonathan Smith as its next head football coach, MSU announced Saturday. This looks like a smart, sensible hire, a relatively young coach who’s already built a program in a power-five conference, and done it at a place where you have to win with less. Oregon State didn’t want to lose him .
The 44-year-old Smith, who’s been the head coach at his alma mater since 2018, takes over for interim coach Harlon Barnett, who replaced Mel Tucker in September after Tucker was fired for misconduct. Barnett said Saturday that he's "possibly" staying at MSU on Smith's staff, though still needs to talk to Smith.
Smith, who emerged as MSU’s top choice this week, coached 20th-ranked Oregon State to an 8-4 season, including a 31-7 loss at sixth-ranked Oregon on Friday night, in a game that began an hour after the Spartans kicked off against Penn State at Ford Field.
“On the field, his teams are tough and physical, yet innovative,” MSU athletic director Alan Haller said in a statement , announcing the hire. “This year, Oregon State has controlled the line of scrimmage on both sides of the ball, ranking among the nation's leaders in both rushing offense and rushing defense. He's shown not only the ability to recruit talented student-athletes who fit his system, but also to develop and maximize players once they're in the program. At his core, he's a quarterbacks coach, and throughout his career he's been instrumental in the development of young quarterbacks, which is essential in today's college football landscape.”
I don’t know a ton about Smith, but coaches who’ve watched what he’s done at Oregon State have been impressed, including one who this week described Smith as a “great coach” and someone who’d be “a home-run hire for MSU.”
The admiration for Smith stems from what he did with the Beavers over six seasons, where he went 34-35 — though 18-7 the last two years — building up a program at place known as a tough spot to win and recruit.
MSU has better facilities, resources and a stronger football pedigree. But, like Oregon State, it’s not the top dog in its conference and likely never will be in terms of NIL payments or prestige. A coach who knows how to build something and win with a tad less than some of his top competitors is a good fit in East Lansing. One of Tucker’s weaknesses was that he’d previously coached at Georgia, Alabama and Ohio State and wanted to model MSU after those programs. That’s a tough ask.
That’s not to put a ceiling on what MSU’s program can become. Chasing a higher ceiling is likely one of the reasons Smith is leaving the school he once quarterbacked and took over six years ago, after stops at Washington, Boise State, Montana and Idaho.
Oregon State reportedly paid Smith close to $4.9 million this past season in the first year of a six-year, $30.6 million contract and was prepared to fight to keep him. MSU likely came with a bigger number — we'll learn it soon — but also the allure of the Big Ten and a campus and facilities that are easier to recruit to. Oregon State was one of two Pac-12 schools left without a chair when the music stopped on the latest round of conference realignment.
At MSU, he’ll be tasked with retooling and reenergizing a program that’s lost its way but isn’t beginning from rock bottom.
RELATED: Couch: MSU football's fall from grace complete, its new coach will have to pick up the pieces
2. Smith’s first recruiting assignment is to re-recruit parts of MSU’s own roster
The college football calendar is insane, making December a whirlwind month and a critical few weeks for coaches, especially new coaches. Smith will have to assemble a staff, while putting together a recruiting class ahead of the early signing period, which begins Dec. 20. Meanwhile, the transfer portal will be open for 30 days, with any MSU player who hasn’t previously transferred able to leave and play elsewhere immediately. That could be as many as 54 scholarship players on the Spartans’ roster, including many of the most promising young names.
In world where logic prevails, signing day would be moved back to only February, allowing coaches to focus on their own players and staffs in December. But for now, Smith will have to do it all at once, while figuring out the lay of the land and sorting out a thorny NIL situation. All in about three weeks. It would make sense if he moves quickly to retain Barnett.
3. Smith’s lack of ties to the Midwest stands out
If there’s concern about Jonathan Smith as MSU’s head football coach, it’s his lack of ties to the Midwest. Smith’s extensive coaching coaching career has never taken him east of Montana. His staff at Oregon State had very few connections to this area of the country. Secondary coach Blue Adams played at Cincinnati. Cornerbacks coach Anthony Perkins spent time at Indiana State and Ohio University in the last decade. But that’s it. The only player on the Beavers’ current roster from this region is a defensive back out of Chicago who was recruited by two coaches who are no longer on staff.
But that staff was built to recruit and win at Oregon State. It’s unlikely the entire crew would come with Smith to MSU. He’ll need to hire at least a couple coaches with experience recruiting Michigan, Ohio and Illinois. MSU has some of those guys on its current staff, including Barnett, who hopes to be retained in some capacity. Perhaps one or two more of them will be re-hired, as well. Keeping a guy like receivers coach Courtney Hawkins would also be a wise move.
Keep in mind, this isn’t an MSU staff that was let go because it failed to bring in any talent. It’s a staff that became a lame-duck group when Mel Tucker was fired for misconduct. There were some good things happening, especially defensively and in the last two recruiting classes, even if it was hard to see in Friday night's season-ending loss to Penn State.
Contact Graham Couch at [email protected]. Follow him on Twitter @Graham_Couch.
Santa Cruz Sentinel
Santa Cruz Warriors beat NBA G League Ignite, move to 3-0
Posted: November 18, 2023 | Last updated: November 18, 2023
Nov. 18—Forward Gui Santos and two-way guard Lester Quinones paced Santa Cruz to a 121-112 win over NBA G League Ignite, 121-112, on Friday night as the Warriors improved to 3-0 at Kaiser Permanente Arena in front of a sold-out crowd.
Santos, on assignment from Golden State, led the Warriors with 29 points and added seven rebounds. Quinones had 27 points, 10 rebounds and five assists.
Guard Kendric Davis and forward Jackson Rowe each scored 15 points for Santa Cruz. Forward/center Trayce Jackson-Davis, also on assignment from Golden State, had 11 points, 10 rebounds, four assists and four shot blocks.
Guards John Jenkins and former Santa Cruz Warrior Jeremy Pargo combined for 39 points to lead Ignite (0-4). Forward Tyler Smith came off the bench and scored 19 points for his fourth straight double-digit scoring effort of the season.
The Warriors won the rebounding battle 51-39, and recorded 24 assists while committing 15 turnovers.
Santa Cruz squares off again with G League Ignite again on Sunday, this time at The Dollar Loan Center in Henderson, Nevada, at 2 p.m.
La Verne Winter Invite: UC Santa Cruz set a school record in the 200 free relay on Day 1 of the La Verne Winter Invite at East Los Angeles College on Friday.
The Banana Slugs' quartet — Coralie Norenberg, Tereza Shea, Maddy Gruender and Kaitlyn Armstrong — posted a finishing time of 1:36.42 to place sixth. It was 0.12 seconds better than the mark set in 2022.
Gruender won the B Final in the 500 Free in 5:02.15 to place ninth overall. Norenberg was 13th in the 50 Free with a time of 24.11.
La Verne Winter Invite: UCSC's 200 free relay team — Massimo Reyna, Bryson Schmid, Adam Sinclair, and Charlie Hussain — took sixth in 1:25.47 on Day 1 of the La Verne Winter Invite at East Los Angeles College on Friday.
The Banana Slugs' Ethan Porter, Karsten Hsiao, Jude Robinson and Schmid were eighth in the 400 medley relay in 3:27.76. Luke Scanlon placed 10th in the 200 individual medley in 1:55.87.
Cabrillo 72, Feather River 40: Jalen Cunningham scored 15 points and the Seahawks opened the Shasta Invitational with a win on Thursday.
Quentin Henry came off the bench and had 10 points and 12 rebounds for the Seahawks. Zavier Sims scored 12 points, and Solomon Tucker had 11 points and eight rebounds.
Cabrillo (5-0), which beat Santa Rosa 76-62 on Friday, faces host Shasta (1-5) on Saturday at 3 p.m.
UCSC 71, at Chapman 65: Fifth-year guard Tyler Kessinger scored 22 points in the Banana Slugs' win Thursday night.
Fifth-year forward Treyson Keating had nine points and nine rebounds for UCSC, which outscored the Panthers 39-29 in the second half.
Junior guard Zino Okah had eight points, eight rebounds, and seven assists for the Banana Slugs (2-1), who play at Pomona-Pitzer (2-0) on Saturday at 3 p.m.
Junior forward Adam Damian had 18 points and seven rebounds to lead the Panthers (2-1).
CCS Singles, Doubles: Aptos senior Holly Hegna and sophomore Coral Collins went 1-1 at the Central Coast Section doubles tournament at Bay Club Courtside in Los Gatos on Tuesday.
Hegna and Collins, the Santa Cruz Coast Athletic League doubles champions, rallied for a 3-6, 6-0, 10-7 win over Carmel's Gia Panetta and Alyssa Moore in the first round, before falling to No. 1 seed Tess Ellingson and Eva Chow of Menlo-Atherton, 6-3, 6-1, in the quarterfinals.
Aptos senior Tiana Smith, the SCCAL singles champion, lost to No. 4 Varsha Jawadi of Aragon 6-1, 6-0 in the first round of the CCS singles tournament on Monday.
Coaches are encouraged to report scores and highlights to [email protected] following games. Please include your name and contact number in the email.
(c)2023 the Santa Cruz Sentinel (Scotts Valley, Calif.) Distributed by Tribune Content Agency, LLC.
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NJ attorney general extends ousted Paterson police chief Ribeiro’s assignment in Trenton
PATERSON — Attorney General Matthew Platkin has decided that ousted Paterson Police Chief Engelbert Ribeiro will spend another six months working at a state law enforcement training commission in Trenton.
Platkin last week signed off on the extension of Ribeiro’s reassignment, a transfer that the former chief’s supporters describe as an exile.
State officials declined to comment on Ribeiro’s extended assignment in Trenton. The city has continued to pay Ribeiro’s salary, which is now $222,000.
Paterson officials object to extension
Paterson’s law director, Aymen Aboushi, sent state officials a letter last week objecting to the decision to keep Ribeiro at the Trenton training commission for another six months.
“This action was taken without notice to the City, and without its consent,” Aboushi wrote.
Ribeiro, who is popular among rank-and-file city cops, was formally appointed chief just 24 days before the Attorney General's Office seized control of the Police Department in March. He had been acting chief for six months before that. As part of the state intervention in the problem-plagued department, Ribeiro was relieved of his duties as chief.
Ribeiro, Mayor Andre Sayegh and civilian police official Mark Bulur have filed a lawsuit seeking to nullify the attorney general's takeover and to restore Ribeiro as chief.
Ribeiro said in the court document that on the morning of the takeover, he arrived at his office and found Platkin and Maj. Frederick Fife of the New Jersey State Police waiting for him. Platkin told Ribeiro to leave his office, the ousted chief said. Later in the day, he said, he was allowed to get his personal belongings from his office.
Fife asked him several times whether he would retire because of the state takeover, Ribeiro said. A month later, Ribeiro said, he was not allowed to attend a swearing-in ceremony for the Police Department’s new recruits and his name was purposefully left off the graduation pamphlet.
In late April, Ribeiro said, he was told he would be assigned to the state police training academy in Sea Girt. Instead, he was sent to the training commission in Trenton in May.
State-appointed law enforcement officials refused to meet with Ribeiro about his reassignment, he said.
In seizing control of the Police Department, Platkin in March said there were “a number of events and concerns” that caused “a crisis in confidence in law enforcement in the city of Paterson.” Neither Platkin nor other state officials cited any misdeeds or mistakes by Ribeiro.
Sayegh has said he plans to restore Ribeiro to the chief’s office when the state intervention ends. The mayor did not comment on Platkin’s decision to extend Ribeiro’s assignment in Trenton.
Earlier: NJ appeals court rules there's no emergency in lawsuit over Paterson police takeover
Growing rifts between Sayegh and Trenton officials
The state intervention has caused a growing rift between Sayegh and Platkin. The attorney general has cited a reduction in violent crime in Paterson during the state takeover, but the mayor last month cited an October spike in homicides, saying residents don’t feel any safer with Platkin in charge.
The split also seems to be affecting Sayegh’s ties with Gov. Phil Murphy, once an ally who affectionately would call the mayor “Andre the Giant.”
Murphy, Platkin and Paterson’s legislative contingent last week gathered at Paterson police headquarters to celebrate the efforts of three detectives whose work led to the arrests of three homicide suspects.
The visit took place while Sayegh was in Atlantic City for the League of Municipalities convention. Political insiders said it was the first time they could recall Murphy holding a public event in Paterson without having Sayegh at his side.
Sayegh’s administration has called into question the legitimacy of the so-called agreement that formalized Ribeiro’s reassignment to the training commission. Sayegh’s law director has noted in a protest letter to the state that even though the document purports to be an agreement between the Attorney General's Office and the city, both parties signing the pact are state employees.
“It is the State’s attempt to enter into a contract with itself,” Aboushi wrote.
Joe Malinconico is editor of Paterson Press. Email: [email protected]
EURO 2024 final tournament draw pots confirmed
Wednesday, November 22, 2023
The pots have been confirmed for the UEFA EURO 2024 final tournament draw in Hamburg on Saturday 2 December.
Article top media content
The UEFA EURO 2024 final tournament draw takes place in Hamburg on Saturday 2 December – the four pots have now been confirmed.
Germany (hosts) Portugal France Spain Belgium England
Hungary Türkiye Romania Denmark Albania Austria
Netherlands Scotland Croatia Slovenia Slovakia Czechia
Italy Serbia Switzerland Play-off winner A Play-off winner B Play-off winner C
How were the draw pots determined?
Hosts Germany are seeded in Pot 1 and automatically allocated to Group A in position A1. The three eventual play-off winners have been assigned to Pot 4.
The other 20 contenders have been allocated to a pot in accordance with their results in the qualifying group stage .
In accordance with Article 23 of the official regulations , seedings are based on overall European Qualifiers rankings, which were determined as follows (results against teams in sixth place were discarded):
a) final position in a group b) points c) goal difference d) goals scored e) away goals scored f) number of wins g) number of away wins h) lower disciplinary points total (3 points for red card including for second booking, 1 point for single yellow card for a player in a match) i) position in overall UEFA Nations League rankings
How does the EURO 2024 draw work?
A further six pots (A, B, C, D, E, F) are required to draw the respective team’s position in each group. Pots B-F contain four balls each to represent the positions available in each group (eg B1, B2, B3 and B4). Pot A contains only three balls for the positions A2, A3 and A4 in Group A, since Germany will occupy position A1.
The draw will be streamed live on UEFA.com and the official UEFA EURO 2024 app from 18:00 CET on Saturday 2 December.