coref | BERT for Coreference Resolution | Natural Language Processing library
kandi X-RAY | coref Summary
kandi X-RAY | coref Summary
This repository contains code and models for the paper, BERT for Coreference Resolution: Baselines and Analysis. Additionally, we also include the coreference resolution model from the paper SpanBERT: Improving Pre-training by Representing and Predicting Spans, which is the current state of the art on OntoNotes (79.6 F1). Please refer to the SpanBERT repository for other tasks. The model architecture itself is an extension of the e2e-coref model.
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Top functions reviewed by kandi - BETA
- Write predictions
- Return the final prediction
- Get the n_best logits from a list of logits
- Compute softmax
- Convert examples to features
- Convert text to printable
- Truncate a sequence pair
- Convert a single example
- Validate the case of the case
- Evaluate the loss function
- Builds a file - like object
- Embed word embedding
- Tokenize text
- Build input function
- Compute the diff between the predicted clusters
- Create training instances
- Convert a JSON file to a list of predictions
- Prints the variables of the given pytorch model
- Creates attention_mask_from_tensor
- Write examples to examples
- Read squad examples
- Return a function that builds TPUEstimator
- Transformer transformer model
- Embedding postprocessor
- Create a custom optimizer
- Get predictions and loss
coref Key Features
coref Examples and Code Snippets
Community Discussions
Trending Discussions on coref
QUESTION
I am working on extracting people and tasks from texts (multiple sentences) and need a way to resolve coreferencing. I found this model, and it seems very promising, but once I installed the required libraries allennlp and allennlp_models and testing the model out for myself I got:
Script:
...ANSWER
Answered 2022-Feb-10 at 16:15The information you are looking for is in 'clusters', where each list corresponds to an entity. Within each entity list, you will find the mentions referring to the same entity. The number are indices that mark the beginning and ending of each coreferential mention. E.g. Paul Allen [0,1] and Allen [24, 24].
QUESTION
I am working on a project where I need to do coreference resolution on a lot of text. In doing so I've dipped my toe into the NLP world and found AllenNLP's coref model.
In general I have a script where I use pandas to load in a dataset of "articles" to be resolved and pass those articles to the predictor.from_path() object to be resolved. Because of the large number of articles that I want to resolve, I'm running this on a remote cluster(though I don't believe that is the source of this problem as this problem also occurs when I run the script locally). That is, my script looks something like this:
ANSWER
Answered 2021-Oct-04 at 14:41I think I figured out two competing and unrelated problems in what I was doing. First, the reason for the unordered printing had to do with SLURM. Using the --unbuffered option fixed the printing problem and made diagnosis much easier. The second problem (which looked like runaway memory usage) had to do with a very long article (aprox 10,000 words) that was just over the max length of the Predictor object. I'm going to close this question now!
QUESTION
I am facing an issue when trying to call spaCy into my Jupyter notebook. When I run
import spacy I get the below:
I have used spaCy before many times with no issue, but I noticed this problem began after I was trying to also install from neuralcoref import Coref and am not sure if that has caused this.
When I go into the terminal and run conda list spacy it looks like spaCy is available:
I do not really understand what the errors are suggesting, but I tried to reinstall murmurhash using conda install -c anaconda murmurhash after which I got this. This is just a screenshot of the first few but there are MANY packages that are allegedly causing the inconsistency:
Following the list of packages causing inconsistencies, I get this:
For reference, I am using MacOS and python 3.7. How can I fix this?
...ANSWER
Answered 2021-Apr-15 at 15:33spacy>=3.0 and neuralcoref are currently not compatible - the Cython API of spaCy's v3 has changed too much. This might be causing conflicts in your environment?
QUESTION
I'm getting the following Error when trying to call my Firebase Cloud Function directly:
...ANSWER
Answered 2021-Mar-25 at 13:38I'm seeing the exact same symptoms while using AngularFireFunctions httpsCallable method. Recently updated firebase sdk to 8.3.1 from 8.2.4 and I suspect this introduced the https callable internal error. Downgrading to 8.2.4 resolves the issue:
npm install firebase@8.2.4 --save
QUESTION
I am looking for a way to extract and merge annotation results from CoreNLP. To specify,
...ANSWER
Answered 2021-Jan-07 at 22:46The coref chains have a sentenceIndex and a beginIndex which should correlate to the position in the sentence. You can use this to correlate the two.
Edit: quick and dirty change to your example code:
QUESTION
I am relatively new to NLP and at the moment I'm trying to extract different phrase scructures in german texts. For that I'm using the Stanford corenlp implementation of stanza with the tregex feature for pattern machting in trees.
So far I didn't have any problem an I was able to match simple patterns like "NPs" or "S > CS". No I'm trying to match S nodes that are immediately dominated either by ROOT or by a CS node that is immediately dominated by ROOT. For that im using the pattern "S > (CS > TOP) | > TOP". But it seems that it doesn't work properly. I'm using the following code:
...ANSWER
Answered 2020-Sep-22 at 22:01A few comments:
1.) Assuming you are using a recent version of CoreNLP (4.0.0+), you need to use the mwt annotator with German. So your annotators list should be tokenize,ssplit,mwt,pos,parse
2.) Here is your sentence in PTB for clarity:
QUESTION
I have a .csv file consists of Imdb sentiment analysis data-set. Each instance is a paragraph. I am using Stanza https://stanfordnlp.github.io/stanza/client_usage.html for getting parse tree for each instance.
...ANSWER
Answered 2020-Jul-29 at 01:12You should only start the server once. It'd be easiest to load the file in Python, extract each paragraph, and submit the paragraphs. You should pass each paragraph from your IMDB to the annotate() method. The server will handle sentence splitting.
QUESTION
I would like to run the sample code for spaCy neuralcoref on jupyter notebook.
ProblemAfter I asked my former questeion, Error to import spaCy neuralcoref module even follwoing the sample code, I have tried to install libraries following another answer to this issue on stackoverflow.
What should I do to run the sample code of spaCy neuralcoref?
Problem 1This part is executable, but notice and output are shown.
...ANSWER
Answered 2020-Jul-11 at 17:11Downgrade to python 3.7. neuralcoref works only for python 3.7 and spaCy 2.1.0.
The best way to fix this in opinion would be alter the requirements.txt of neuralcoref and change spacy>=2.1.0,<2.2.0 to spacy==2.1.0
Hope that helps.
QUESTION
I need to implement a solution which can recognize pronouns associated with the noun in a sentence. Say I have an paragraph about a person, I wanna count how many times the person has been referenced (name or any other pronoun). I want to implement this is Python.
After some research I came across neuralcoref and though it could be useful. After several attempts I'm still getting stuck because the kernel keeps dying.
It would be great if someone can help with this problem. I am also open to suggestions about other libraries/resources I could use to implement this.
Thanks!
This is the code I used:
...ANSWER
Answered 2020-Jul-09 at 06:30You need to use spaCy version 2.1.0 and python version 3.7 for neuralcoref to work. See here for reference
QUESTION
I want to compare the input data of a user, with my firestore record (data that is already stored in my Firebase.
...ANSWER
Answered 2020-May-04 at 15:23Simply convert the document into a POJO. There's a method toObject(Class valueType) in Firestore API which resolves that.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install coref
export data_dir=</path/to/data_dir>
./setup_all.sh: This builds the custom kernels
This assumes access to OntoNotes 5.0. ./setup_training.sh <ontonotes/path/ontonotes-release-5.0> $data_dir. This preprocesses the OntoNotes corpus, and downloads the original (not finetuned on OntoNotes) BERT models which will be finetuned using train.py.
Experiment configurations are found in experiments.conf. Choose an experiment that you would like to run, e.g. bert_base
Note that configs without the prefix train_ load checkpoints already tuned on OntoNotes.
Training: GPU=0 python train.py <experiment>
Results are stored in the log_root directory (see experiments.conf) and can be viewed via TensorBoard.
Evaluation: GPU=0 python evaluate.py <experiment>. This currently evaluates on the dev set.
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