FARM | Harvesting language models | Natural Language Processing library
kandi X-RAY | FARM Summary
kandi X-RAY | FARM Summary
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
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Top functions reviewed by kandi - BETA
- Cross - validation .
- Start holdout of a doc classifier .
- Demonstrates how to cross validation
- Convert natural questions into features .
- Demonstrates how to learn a doc class with an early stopping algorithm .
- This method is used to create a sample of a sample .
- Train the model .
- Demonstrates how to run a dense pass on a neural network .
- Example of reading from scratch .
- Run an experiment .
FARM Key Features
FARM Examples and Code Snippets
Lines of Code : 63License : Permissive (Apache-2.0)
Copyapply plugin: 'devicefarm'
devicefarm {
// Required. The Project must already exist. You can create a project in the AWS console.
projectName "My Project" // required: Must already exist.
// Optional. Defaults to "Top Devices"
devi 
Lines of Code : 4License : Strong Copyleft (CC BY-SA 4.0)Copy@receiver([post_save, post_delete], sender=Batch)
def update_batch_count(sender, instance, **kwargs):
instance.farm_id.update_batch_count()
pip install -U farm-haystack pinecone-client
document_store = PineconeDocumentStore(
api_key="", # from https://app.pinecone.io
environment="us-west1-gcp"
)
retriever = EmbeddingRetriever(
document_stor# Use a template literal string formatted almost exactly as 'WYSIWYG'
VERSE = """
Verse: {verse}
Old MacDonald had a farm
E-I-E-I-O
And on his farm he had {amount} {animal}s
With a {noise} {noise} here
And a {noise} {noise} there
Here a {ndef rent_book(self, **kwargs):
# if is_return:
# return False
if not kwargs:
raise KeyError("Must search on at least one of fname, sname, or title.")
for x in self.lstBooks:
if not (x['avail'] and all(x[kform = ProjectDetailForm(project_id, request.POST, instance = project)
form = ProjectDetailForm(request.POST, instance = project)
else:
project = get_object_or_404(Project, pk=pout = df.groupby(['Animal ID', 'Farm ID'], as_index=False)['Assigned'] \
.apply(lambda x: x.max() - x.min())
print(out)
# Output
Animal ID Farm ID Assigned
0 10 10 0.11
1 10 20 0.06
2 from datetime import datetime
...
class TestBatch(TestCase):
def setUp(self):
self.batch1 = Batch.objects.create(
...
start_date=datetime(2021, 11, 26, 14, 20, 4),
...
)
df["Output"] = df["Model 1"].merge(df["Model 2"],
how="left",
left_on=["Lat", "Lon"],
right_on=["X", "Y"],
Community Discussions
Trending Discussions on FARM
QUESTION
Given a list of Strings:
...ANSWER
Answered 2022-Mar-22 at 07:13This problem should be solved easily using a trie.
The trie node should basically keep a track of 2 things:
- Child nodes
- Count of prefixes ending at current node
Insert all strings in the trie, which will be done in O(string length * number of strings). After that, simply traversing the trie, you can hash the prefixes based on the count as per your use case. For suffixes, you can use the same approach, just start traversing the strings in reverse order.
Edit:
On second thought, trie might be the most efficient way, but a simple hashmap implementation should also work here. Here's an example to generate all prefixes with count > 1.
QUESTION
I have a set of legacy data that has aimed to match companies together (based on a number of different factors). However, I would like to do a bit of QA on the matches just based on the names that came from the different profiles (and may have small differences).
Assuming a dataset as follows:
Match ID Name Left Name Right 1 LemonFarms Lemon Farms Inc 2 Peachtree PeachTree Farms 3 Tomato Grove Orange Cheetah FarmIs their an algorithmic or pythonic way to know for example that 1 and 2 are probably well matched and 3 badly matched? This, without any ML etc.
I would imagine I need to create a score somehow and knock out on this. However, would love some input if their is best practice.
Some ideas I have had is to look through patterns, compare letters in sets etc. However, at a lost to execute them.
...ANSWER
Answered 2022-Feb-16 at 14:36You can try fuzzywuzzy with score , then you just need to set up score limit for cut
QUESTION
Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.
Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.
ANSWER
Answered 2022-Feb-03 at 09:29I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081
QUESTION
This question is part of a previous one that I had (How to get the average temperature for N days prior to a specific date in R?), however things have complicated a bit (at least for me).
I have two datasets: one dataset (A) has the temperatures for each day, and on the other dataset (B) I have the individuals id and the date of birth (dob). I need to get the average temperature for the last 3 days prior to the dob of each individual. For example: if individual 1 was born in 02/20/2021, I need the average temperature from 02/17/2021 to 02/19/2021. However, I also have data coming from different weather station id (in df A), that needs to correspond with the right farm (in df B). Is there a way I could do that in R, so my output would be ind | dob | avg_temp, accounting for the right weather station. Here is one example data (in my real case, my data has a very large number of days, individuals, farms and station):
...ANSWER
Answered 2022-Feb-08 at 01:02Thanks for the clarification. This extension of the function includes the location to calculate the average temperature. This assumes farm and station have corresponding values despite having different column names, as in your example. I've used mapply here to make sure the elements line up (i.e. x[1] goes with location[1], x[2] with location[2] and so on). That way if a particular date of birth only happens in one location it won't be calculated for all locations. I believe you could use lapply as well.
QUESTION
I have a few files that are named after rural properties like the following:
...ANSWER
Answered 2022-Jan-25 at 16:58This should do it:
QUESTION
This is my first stack overflow question, so if I am presenting something wrong, please let me know. I am pretty new to computer programming, so I just have a small webpage where I am just implementing things that I am learning.
I made a little quiz with random trivia multiple choice questions you can take if you press a button. I am using window prompts to ask the questions and get the answers, and I have all of the questions and answers stored as objects with question/prompt and answer pairs. All of those objects are stored in an array in a variable called shortQuizPrompts. I already have the quiz working and everything, aka., It tells you after every question if you got the answer to that question right or wrong, and it gives you a grade afterwards... I also have it set up so that if you enter an answer that is not "a", "b", "c", or "d", it lets you know that it isnt a valid answer. Those sorts of things.
As of right now, you can choose how many questions long you want the quiz to be out of the 24 total questions I have so far. It just asks the questions in the order that they are stored in the array. For example, you will never be asked the last question in the array if you do not choose for the quiz to be the full 24 questions long. However, I want to make the quiz ask the questions in a random order, while also removing those questions from the array as to not ask the same question multiple times.
I have tried increasing the iterator while looping through the array to a random number from 0 to the length of however many questions they chose. Then checking to see if the iterator was larger than the length of the number of questions they chose, it would decrease the iterator until it found a question that is still in the array that it could ask...
If anyone knows how to go about doing that, it would be great. Sorry for the long question btw. I am pretty new to coding, so this is probably a simple answer, but I digress. I'm pretty sure I did everything right. Thx.
...ANSWER
Answered 2022-Jan-12 at 01:03You can shuffle the shortQuizPrompts array before starting the quiz. Array shuffle details can be found in this answer.
QUESTION
I have a list of strings, let's say:
...ANSWER
Answered 2022-Jan-08 at 23:21You can let Pandas do the work like so:
QUESTION
I am trying to edit a discord bot made in python (I stored data initially in python) and transferring it to javascript (node.js) and can't feature out while connecting to my old db why findOne giving me null while providing proper discord id.
Without anything inside
Code
ANSWER
Answered 2021-Sep-08 at 07:07you can't pass an id as a number, you will have to use ObjectId to convert the id to an instanceof ObjectId
Change your code like this
QUESTION
I have a 1GB JSON file I like to convert to CSV format. The file contains information about UK company people with significant control (PSC). file source: http://download.companieshouse.gov.uk/en_pscdata.html
here is a data snippet of PSC Data product:
...ANSWER
Answered 2021-Dec-05 at 18:08Since you are selecting bits of data from different levels of the input objects, you will need to specify the selection more precisely.
As your input consists of a stream of JSON objects, let's start with a function for reading one of those objects:
QUESTION
Using pandas Library, I made dictionaries that are nested in a list from file “german_words.csv”. (for Info: “german_words.csv” is file with German words and corresponding English translated words)
german_words.csv (It's just sample, current file contains thousands of words):
...ANSWER
Answered 2021-Nov-11 at 06:25Create Series by column Deutsch like index, select column English and then convert to dictionaries:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install FARM
You can use FARM like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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