transformers | 🤗 Transformers : State-of-the-art Machine Learning | Natural Language Processing library
kandi X-RAY | transformers Summary
kandi X-RAY | transformers Summary
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
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Top functions reviewed by kandi - BETA
- Generates a beam search output .
- Perform beam search .
- Performs the Bigbird block - sparse attention .
- Instantiate a pipeline .
- Fetches the given model .
- Train a discriminator .
- Perform beam search .
- Convert bort checkpoint to pytorch .
- Convert a Segformer checkpoint checkpoint .
- Wrapper for selftrain .
transformers Key Features
transformers Examples and Code Snippets
Lines of Code : 33License : Permissive (Apache-2.0)
Copy>>> from transformers import pipeline
# Allocate a pipeline for sentiment-analysis
>>> classifier = pipeline('sentiment-analysis')
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
[ Args:
n_layers (`int`): The number of layers of the model.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices ca Example:
```python
>>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/lib import sys
import json
from torch.utils.data import DataLoader
from sentence_transformers import SentenceTransformer, LoggingHandler, util, models, evaluation, losses, InputExample
import logging
from datetime import datetime
import gzip
import os
im """
This script contains an example how to extend an existent sentence embedding model to new languages.
Given a (monolingual) teacher model you would like to extend to new languages, which is specified in the teacher_model_name
variable. We train a """
This script tests the approach on the BUCC 2018 shared task on finding parallel sentences:
https://comparable.limsi.fr/bucc2018/bucc2018-task.html
You can download the necessary files from there.
We have used it in our paper (https://arxiv.org/ ner = pipeline("ner", aggregation_strategy="simple", model="dbmdz/bert-large-cased-finetuned-conll03-english") # Named Entity Recognition (NER)
from transformers import DebertaTokenizer, DebertaModel
import torch
# downloading the models
tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
model = DebertaModel.from_pretrained("microsoft/deberta-base")
# tokenizinmodel.embeddings
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
print(model.embeddings)
# output is
BertEmbeddings(
(word_embeddings): Embedding(30522, 768, padding_idx=0)
(position_embeddings): Embedding(512Community Discussions
Trending Discussions on transformers
QUESTION
I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:
AttributeError: module
'__main__'has no attribute 'GensimWord2VecVectorizer'
I am creating the model on Google Colab.
Code in Jupyter Notebook:
...ANSWER
Answered 2022-Feb-24 at 11:48Import GensimWord2VecVectorizer in your Flask Web app python file.
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
I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:
Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help
Dear customer.... etc
The highlighted sentence would be:
However, this morning the door does not lock properly.
- What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
- What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
- What resources are available to research how to implement this in Python (using tensorflow or pytorch)
I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.
...ANSWER
Answered 2022-Feb-07 at 10:21This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.
The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.
I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).
This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.
QUESTION
I learned that you can redefine ContT from transformers such that the r type parameter is made implicit (and may be specified explicitly using TypeApplications), viz.:
ANSWER
Answered 2022-Feb-01 at 19:28Nobody uses this (invisible dependent quantification) for this purpose (where the dependency is not used) but it is the same as giving a Type -> .. parameter, implicitly.
QUESTION
I am new to Arrow and try to establish my mental model of how its effects system works; in particular, how it leverages Kotlin's suspend system. My very vague understanding is as follows; if would be great if someone could confirm, clarify, or correct it:
Because Kotlin does not support higher-kinded types, implementing applicatives and monads as type classes is cumbersome. Instead, arrow derives its monad functionality (bind and return) for all of Arrow's monadic types from the continuation primitive offered by Kotlin's suspend mechanism. Ist this correct? In particular, short-circuiting behavior (e.g., for nullable or either) is somehow implemented as a delimited continuation. I did not quite get which particular feature of Kotlin's suspend machinery comes into play here.
If the above is broadly correct, I have two follow-up questions: How should I contain the scope of non-IO monadic operations? Take a simple object construction and validation example:
...ANSWER
Answered 2022-Jan-31 at 08:52I don't think I can answer everything you asked, but I'll do my best for the parts that I do know how to answer.
What is the recommended way to implement non-IO monad comprehensions in Arrow without making all functions into suspend functions? Or is this actually the way to go?
you can use nullable.eager and either.eager respectively for pure code. Using nullable/either (without .eager) allows you to call suspend functions inside. Using eager means you can only call non-suspend functions. (not all effectual functions in kotlin are marked suspend)
Second: If in addition to non-IO monads (nullable, reader, etc.), I want to have IO - say, reading in a file and parsing it - how would i combine these two effects? Is it correct to say that there would be multiple suspend scopes corresponding to the different monads involved, and I would need to somehow nest these scopes, like I would stack monad transformers in Haskell?
You can use extension functions to emulate Reader. For example:
QUESTION
I'm using jest to test a react TypeScript app.
This is the test I'm running:
...ANSWER
Answered 2022-Jan-22 at 22:37react-markdown is shipped as js, add babel-jest as a transformer in your jest config
QUESTION
I found that Reader is implemented based on ReaderT using Identity. Why don't make Reader first and then make ReaderT? Is there specific reason to implement that way?
ANSWER
Answered 2022-Jan-11 at 17:11They are the same data type to share as much code as possible between Reader and ReaderT. As it stands, only runReader, mapReader, and withReader have any special cases. And withReader doesn't have any unique code, it's just a type specialization, so only two functions actually do anything special for Reader as opposed to ReaderT.
You might look at the module exports and think that isn't buying much, but it actually is. There are a lot of instances defined for ReaderT that Reader automatically has as well, because it's the same type. So it's actually a fair bit less code to have only one underlying type for the two.
Given that, your question boils down to asking why Reader is implemented on top of ReaderT, and not the other way around. And for that, well, it's just the only way that works.
Let's try to go the other direction and see what goes wrong.
QUESTION
I am getting the following error : attributeerror: 'dataframe' object has no attribute 'data_type'" . I am trying to recreate the code from this link which is based on this article with my own dataset which is similar to the article
ANSWER
Answered 2022-Jan-10 at 08:41The error means you have no data_type column in your dataframe because you missed this step
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
...ANSWER
Answered 2021-Dec-25 at 21:51There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.
As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:
QUESTION
Given an sklearn tranformer t, is there a way to determine whether t changes columns/column order of any given input dataset X, without applying it to the data?
For example with t = sklearn.preprocessing.StandardScaler there is a 1-to-1 mapping between the columns of X and t.transform(X), namely X[:, i] -> t.transform(X)[:, i], whereas this is obviously not the case for sklearn.decomposition.PCA.
A corollary of that would be: Can we know, how the columns of the input will change by applying t, e.g. which columns an already fitted sklearn.feature_selection.SelectKBest chooses.
I am not looking for solutions to specific transformers, but a solution applicable to all or at least a wide selection of transformers.
Feel free to implement your own Pipeline class or wrapper if necessary.
...ANSWER
Answered 2021-Nov-23 at 15:01I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.
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Install transformers
You can use transformers 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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