Feednite | Fortnite news RSS Feed generator | Artificial Intelligence library

 by   Qrivi JavaScript Version: Current License: MIT

kandi X-RAY | Feednite Summary

Feednite is a JavaScript library typically used in Artificial Intelligence applications. Feednite has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
An RSS feed generator for Fortnite news I wrote in 59 minutes, because much like Psyonix with Rocket League, Epic Games is not providing an RSS feed to follow up on their news updates either. The script generates a very complete and valid RSS feed on the fly and takes 3 optional parameters.
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                                                                                  Feednite Key Features

                                                                                  Fortnite news RSS Feed generator

                                                                                  Feednite Examples and Code Snippets

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                                                                                  Community Discussions

                                                                                  Trending Discussions on Artificial Intelligence

                                                                                  Space Complexity in Breadth First Search (BFS) Algorithm
                                                                                  chevron right
                                                                                  Why is there an additional "None" dimension in the tensor shape when uploading a dataset to Activeloop Hub?
                                                                                  chevron right
                                                                                  What does stopping the runtime while uploading a dataset to Hub cause?
                                                                                  chevron right
                                                                                  What is the loss function used in Trainer from the Transformers library of Hugging Face?
                                                                                  chevron right
                                                                                  I do not split well in pytorch
                                                                                  chevron right
                                                                                  Alan AI Error Uncaught Error: The Alan Button instance has already been created. There cannot be two Alan Button instances created at the same time
                                                                                  chevron right
                                                                                  KeyedVectors\' object has no attribute \'wv for gensim 4.1.2
                                                                                  chevron right
                                                                                  Can't initialize object of Detector class from py-feat
                                                                                  chevron right
                                                                                  How to load an onnx model using ONNX.js
                                                                                  chevron right
                                                                                  Spacy NER not recognising NAME
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                                                                                  QUESTION

                                                                                  Space Complexity in Breadth First Search (BFS) Algorithm
                                                                                  Asked 2022-Apr-11 at 08:08

                                                                                  According to Artificial Intelligence A Modern Approach - Stuart J. Russell , Peter Norvig (Version 4), space complexity of BFS is O(b^d), where 'b' is branching factor and 'd' is depth.

                                                                                  Complexity of BFS is obtained by this assumption: we store all nodes till we arrive to target node, in other word: 1 + b + b^2 + b^3 + ... + b^d => O(b^d)

                                                                                  But why should we store all nodes? don't we use queue for implementation?

                                                                                  If we use queue, don't need to store all nodes, because we enqueue and dequeue some nodes in steps, then when we find target node(s), we can say some nodes are in queue (but not all of them).

                                                                                  Is my understanding wrong?

                                                                                  ANSWER

                                                                                  Answered 2022-Apr-10 at 06:16

                                                                                  At any moment while we apply BFS, the queue would have at most two levels of nodes, for example if we just started searching in depth d, then the queue now contains all nodes at depth d and as we proceed the queue would finish all nodes at depth d and have all nodes at depth d+1, so at any moment we have O(b^d) space.

                                                                                  Also 1+b+b^2+...+b^d = (b^(d+1)-1)/(b-1).

                                                                                  Source https://stackoverflow.com/questions/71814173

                                                                                  QUESTION

                                                                                  Why is there an additional "None" dimension in the tensor shape when uploading a dataset to Activeloop Hub?
                                                                                  Asked 2022-Mar-24 at 23:15

                                                                                  I am trying to upload an image datset to Hub (dataset format with an API for creating, storing, & collaborating on AI datasets). I only uploaded part of the dataset, however upon inspecting the uploaded data I noticed that there was an additional None dimension in the tensor shape. Can someone explain why this occurred?

                                                                                  I am using the following tensor relationship:

                                                                                  ds 
                                                                                  -> images (htype = image)
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-24 at 23:15

                                                                                  The none dimension is present because some of the images might have three channels and the others have four, so dynamic dimensions are shown as None.

                                                                                  Source https://stackoverflow.com/questions/71610475

                                                                                  QUESTION

                                                                                  What does stopping the runtime while uploading a dataset to Hub cause?
                                                                                  Asked 2022-Mar-24 at 01:06

                                                                                  I am getting the following error while trying to upload a dataset to Hub (dataset format for AI) S3SetError: Connection was closed before we received a valid response from endpoint URL: "<...>".

                                                                                  So, I tried to delete the dataset and it is throwing this error below.

                                                                                  CorruptedMetaError: 'boxes/tensor_meta.json' and 'boxes/chunks_index/unsharded' have a record of different numbers of samples. Got 0 and 6103 respectively.

                                                                                  Using Hub version: v2.3.1

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-24 at 01:06

                                                                                  Seems like when you were uploading the dataset the runtime got interrupted which led to the corruption of the data you were trying to upload. Using force=True while deleting should allow you to delete it.

                                                                                  For more information feel free to check out the Hub API basics docs for details on how to delete datasets in Hub.

                                                                                  If you stop uploading a Hub dataset midway through your dataset will be only partially uploaded to Hub. So, you will need to restart the upload. If you would like to re-create the dataset, you can use the overwrite = True flag in hub.empty(overwrite = True). If you are making updates to an existing dataset, you should use version control to checkpoint the states that are in good shape.

                                                                                  Source https://stackoverflow.com/questions/71595867

                                                                                  QUESTION

                                                                                  What is the loss function used in Trainer from the Transformers library of Hugging Face?
                                                                                  Asked 2022-Mar-23 at 10:12

                                                                                  What is the loss function used in Trainer from the Transformers library of Hugging Face?

                                                                                  I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face.

                                                                                  In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class. However, if I do not do the method override and use the Trainer to fine tine a BERT model directly for sentiment classification, what is the default loss function being use? Is it the categorical crossentropy? Thanks!

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-23 at 10:12

                                                                                  It depends! Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.

                                                                                  You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:

                                                                                  loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

                                                                                  which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.

                                                                                  Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.

                                                                                  The code relevant for defining a loss function looks like this:

                                                                                  if labels is not None:
                                                                                      if self.config.problem_type is None:
                                                                                          if self.num_labels == 1:
                                                                                              self.config.problem_type = "regression"
                                                                                          elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                                                                                              self.config.problem_type = "single_label_classification"
                                                                                          else:
                                                                                              self.config.problem_type = "multi_label_classification"
                                                                                  
                                                                                      if self.config.problem_type == "regression":
                                                                                          loss_fct = MSELoss()
                                                                                          if self.num_labels == 1:
                                                                                              loss = loss_fct(logits.squeeze(), labels.squeeze())
                                                                                          else:
                                                                                              loss = loss_fct(logits, labels)
                                                                                      elif self.config.problem_type == "single_label_classification":
                                                                                          loss_fct = CrossEntropyLoss()
                                                                                          loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
                                                                                      elif self.config.problem_type == "multi_label_classification":
                                                                                          loss_fct = BCEWithLogitsLoss()
                                                                                          loss = loss_fct(logits, labels)
                                                                                  
                                                                                  

                                                                                  Based on this information, you should be able to either set the correct loss function yourself (by changing model.config.problem_type accordingly), or otherwise at least be able to determine whichever loss will be chosen, based on the hyperparameters of your task (number of labels, label scores, etc.)

                                                                                  Source https://stackoverflow.com/questions/71581197

                                                                                  QUESTION

                                                                                  I do not split well in pytorch
                                                                                  Asked 2022-Mar-21 at 09:57

                                                                                  I would like to do a tensor split in pytorch. However, I get an error message because I can't get the splitting to work.
                                                                                  The behavior I want is to split the input data into two Fully Connected layers. I then want to create a model that combines the two Fully Connected layers into one. I believe the error is due to a wrong code in x1, x2 = torch.tensor_split(x,2)

                                                                                  import torch
                                                                                  from torch import nn, optim
                                                                                  import numpy as np
                                                                                  from matplotlib import pyplot as plt
                                                                                  
                                                                                  class Regression(nn.Module):
                                                                                      def __init__(self):
                                                                                          super().__init__()
                                                                                          self.linear1 = nn.Linear(1, 32)
                                                                                          self.linear2 = nn.Linear(32, 16)
                                                                                          self.linear3 = nn.Linear(16*2, 1)
                                                                                  
                                                                                      def forward(self, x):
                                                                                          x1, x2 = torch.tensor_split(x,2)
                                                                                          x1 = nn.functional.relu(self.linear1(x1))
                                                                                          x1 = nn.functional.relu(self.linear2(x1))
                                                                                  
                                                                                          x2 = nn.functional.relu(self.linear1(x2))
                                                                                          x2 = nn.functional.relu(self.linear2(x2))
                                                                                          cat_x = torch.cat([x1, x2], dim=1)
                                                                                  
                                                                                          cat_x = self.linear3(cat_x)
                                                                                          return cat_x
                                                                                  
                                                                                  def train(model, optimizer, E, iteration, x, y):
                                                                                      losses = []
                                                                                      for i in range(iteration):
                                                                                          optimizer.zero_grad()                   # 勾配情報を0に初期化
                                                                                          y_pred = model(x)                       # 予測
                                                                                          loss = E(y_pred.reshape(y.shape), y)    # 損失を計算(shapeを揃える)
                                                                                          loss.backward()                         # 勾配の計算
                                                                                          optimizer.step()                        # 勾配の更新
                                                                                          losses.append(loss.item())              # 損失値の蓄積
                                                                                          print('epoch=', i+1, 'loss=', loss)
                                                                                      return model, losses
                                                                                  
                                                                                  x = np.random.uniform(0, 10, 100)                                   # x軸をランダムで作成
                                                                                  y = np.random.uniform(0.9, 1.1, 100) * np.sin(2 * np.pi * 0.1 * x)  # 正弦波を作成
                                                                                  x = torch.from_numpy(x.astype(np.float32)).float()                  # xをテンソルに変換
                                                                                  y = torch.from_numpy(y.astype(np.float32)).float()                  # yをテンソルに変換
                                                                                  X = torch.stack([torch.ones(100), x], 1)   
                                                                                  
                                                                                  net = Regression()
                                                                                  
                                                                                  optimizer = optim.RMSprop(net.parameters(), lr=0.01)                # 最適化にRMSpropを設定
                                                                                  E = nn.MSELoss()                                                    # 損失関数にMSEを設定
                                                                                  
                                                                                  net, losses = train(model=net, optimizer=optimizer, E=E, iteration=5000, x=X, y=y)
                                                                                  

                                                                                  error message

                                                                                  /usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
                                                                                     1846     if has_torch_function_variadic(input, weight, bias):
                                                                                     1847         return handle_torch_function(linear, (input, weight, bias), input, weight, bias=bias)
                                                                                  -> 1848     return torch._C._nn.linear(input, weight, bias)
                                                                                     1849 
                                                                                     1850 
                                                                                  
                                                                                  RuntimeError: mat1 and mat2 shapes cannot be multiplied (50x2 and 1x32)
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-21 at 09:57
                                                                                  Tl;dr

                                                                                  Specify dim=1 in torch.tensor_split(x,2) .

                                                                                  Explanation

                                                                                  The x comes from two tensors with the shape [100,1] stacked at dim 1, so its shape is [100, 2]. After applying tensor_split, you get two tensors both with shape [50, 2].

                                                                                  print(x.shape) # torch.Size([100, 2])
                                                                                  print(torch.tensor_split(X,2)[0].shape) # torch.Size([50, 2])
                                                                                  

                                                                                  The error occurred because linear1 only accepts tensors with shape [BATCH_SIZE,1] as the input, but a tensor with shape [50, 2] was passed in.

                                                                                  If your intention was to split the array of random numbers and the array of all ones, change torch.tensor_split(x,2) to torch.tensor_split(x,2,dim=1), which produces two tensors with the shape [100,1].

                                                                                  Source https://stackoverflow.com/questions/71554131

                                                                                  QUESTION

                                                                                  Alan AI Error Uncaught Error: The Alan Button instance has already been created. There cannot be two Alan Button instances created at the same time
                                                                                  Asked 2022-Mar-21 at 09:48

                                                                                  I am developing an E-commerce website AI powered Voice Command Using Alan AI. But Whenever I come back from another route, there's a blank page appears.and this error message shows in the console: "Uncaught Error: The Alan Button instance has already been created. There cannot be two Alan Button instances created at the same time". What can I do? my code is given below:

                                                                                  const Alan = () => {
                                                                                  
                                                                                      useEffect(() => {
                                                                                          alanBtn({
                                                                                              key: alanKey,
                                                                                              onCommand: ({ command }) => {
                                                                                                  if (command === 'testCommand') {
                                                                                                      alert('This code was executed');
                                                                                                  }
                                                                                              }
                                                                                          })
                                                                                      }, [])
                                                                                  
                                                                                      return (
                                                                                          
                                                                                  
                                                                                          
                                                                                      );
                                                                                  };
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-21 at 09:48

                                                                                  It's critical but easy...!

                                                                                  Use requestAnimationFrame for your webpage visual changes.

                                                                                  If run as a requestAnimationFrame callback, this will be run at the start of the frame.

                                                                                  const Alan = () => {

                                                                                  useLayoutEffect(() => {
                                                                                      function updateScreen(time) {
                                                                                          // Make visual updates here.
                                                                                          alanBtn({
                                                                                              key: alanKey,
                                                                                              onCommand: ({ command }) => {
                                                                                                  if (command === 'testCommand') {
                                                                                                      alert('This code was executed');
                                                                                                  }
                                                                                              }
                                                                                          })
                                                                                      }
                                                                                  
                                                                                      requestAnimationFrame(updateScreen);
                                                                                  }, [])
                                                                                  
                                                                                  return (
                                                                                      
                                                                                  
                                                                                      
                                                                                  );
                                                                                  

                                                                                  };

                                                                                  Source https://stackoverflow.com/questions/71548257

                                                                                  QUESTION

                                                                                  KeyedVectors\' object has no attribute \'wv for gensim 4.1.2
                                                                                  Asked 2022-Mar-20 at 19:43

                                                                                  i have migrated from gensim 3.8.3 to 4.1.2 and i am using this

                                                                                  claim = [token for token in claim_text if token in w2v_model.wv.vocab]

                                                                                  reference = [token for token in ref_text if token in w2v_model.wv.vocab]

                                                                                  i am not sure how to replace w2v_model.wv.vocab to newer attribute and i am getting this error

                                                                                  KeyedVectors' object has no attribute 'wv' can anyone please help.

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-20 at 19:43

                                                                                  You only use the .wv property to fetch the KeyedVectors object from another more complete algorithmic model, like a full Word2Vec model (which contains a KeyedVectors in its .wv attribute).

                                                                                  If you're already working with just-the-vectors, there's no need to request the word-vectors subcomponent. Whatever you were going to do, you just do to the KeyedVectors directly.

                                                                                  However, you're also using the .vocab attribute, which has been replaced. See the migration FAQ for more details:

                                                                                  https://github.com/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4#4-vocab-dict-became-key_to_index-for-looking-up-a-keys-integer-index-or-get_vecattr-and-set_vecattr-for-other-per-key-attributes

                                                                                  (Mainly: instead of doing an in w2v_model.wv.vocab, you may only need to do in kv_model or in kv_model.key_to_index.)

                                                                                  Source https://stackoverflow.com/questions/71544767

                                                                                  QUESTION

                                                                                  Can't initialize object of Detector class from py-feat
                                                                                  Asked 2022-Mar-19 at 20:41

                                                                                  I try to detecting FEX from videos according to this instruction: https://py-feat.org/content/detector.html#detecting-fex-from-videos

                                                                                  But I can't initialize object of Detector class. Code that I use:

                                                                                  from feat import Detector
                                                                                  
                                                                                  face_model = "retinaface"
                                                                                  landmark_model = "mobilenet"
                                                                                  au_model = "rf"
                                                                                  emotion_model = "resmasknet"
                                                                                  detector = Detector(face_model=face_model, landmark_model=landmark_model, au_model=au_model,
                                                                                                      emotion_model=emotion_model)
                                                                                  
                                                                                  if __name__ == '__main__':
                                                                                      pass
                                                                                  

                                                                                  And I get the following errors:

                                                                                  C:\Users\User\AppData\Roaming\Python\Python39\site-packages\nilearn\input_data\__init__.py:27: FutureWarning: The import path 'nilearn.input_data' is deprecated in version 0.9. Importing from 'nilearn.input_data' will be possible at least until release 0.13.0. Please import from 'nilearn.maskers' instead.
                                                                                    warnings.warn(message, FutureWarning)
                                                                                  Loading Face Detection model:  retinaface
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\mobilenet0.25_Final.pth
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\mobilenet_224_model_best_gdconv_external.pth.tar
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_pca_all_emotio.joblib
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_pca_all_emotio.joblib
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_scalar_aus.joblib
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\RF_568.joblib
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_pca_all_emotio.joblib
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\hog_scalar_aus.joblib
                                                                                  Using downloaded and verified file: C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\ResMaskNet_Z_resmasking_dropout1_rot30.pth
                                                                                  Loading Face Landmark model:  mobilenet
                                                                                  Loading au model:  rf
                                                                                  Loading emotion model:  resmasknet
                                                                                  Traceback (most recent call last):
                                                                                    File "C:\Users\User\Desktop\DetectFEXFromVideos\main.py", line 7, in 
                                                                                      detector = Detector(face_model=face_model, landmark_model=landmark_model, au_model=au_model,
                                                                                    File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\detector.py", line 227, in __init__
                                                                                      self.emotion_model = ResMaskNet()
                                                                                    File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\emo_detectors\ResMaskNet\resmasknet_test.py", line 748, in __init__
                                                                                      torch.load(
                                                                                    File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\torch\serialization.py", line 713, in load
                                                                                      return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
                                                                                    File "C:\Users\User\AppData\Roaming\Python\Python39\site-packages\torch\serialization.py", line 938, in _legacy_load
                                                                                      typed_storage._storage._set_from_file(
                                                                                  RuntimeError: unexpected EOF, expected 32606425 more bytes. The file might be corrupted.
                                                                                  
                                                                                  Process finished with exit code 1
                                                                                  

                                                                                  I'm new to Python, that's why I didn't change any arguments in object initialize. Don't understand what each means.

                                                                                  P.S. And maybe anyone know, how to fix problem in 2 first rows?

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-19 at 20:41

                                                                                  It looks like one of your files was corrupted.

                                                                                  You can try to solve the problem by opening the directory C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\ and deleting the file ResMaskNet_Z_resmasking_dropout1_rot30.pth.

                                                                                  Then run again the code and it should redownload the deleted file.

                                                                                  The warning in the first two lines is just a warning, it's saying that some of the code in the library nilearn is deprecated. Most of the times you would just ignore this line, this will be probably fixed by the coders of nilearn in a future patch.

                                                                                  Source https://stackoverflow.com/questions/71541634

                                                                                  QUESTION

                                                                                  How to load an onnx model using ONNX.js
                                                                                  Asked 2022-Mar-08 at 09:10

                                                                                  I am trying to import an ONNX model using onnxjs, but I get the below error:

                                                                                  Uncaught (in promise) TypeError: cannot resolve operator 'Cast' with opsets: ai.onnx v11
                                                                                  

                                                                                  Below shows a code snippet from my html file.

                                                                                  
                                                                                     
                                                                                    
                                                                                      
                                                                                      
                                                                                      
                                                                                      
                                                                                    
                                                                                  
                                                                                  

                                                                                  How to solve this?

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-01 at 20:37

                                                                                  This will load Resnet 50 model

                                                                                  const sess = new onnx.InferenceSession()
                                                                                  async function test(){
                                                                                    console.time("loading model")
                                                                                    await sess.loadModel('https://microsoft.github.io/onnxjs-demo/resnet50v2.onnx')
                                                                                    console.timeEnd("loading model")
                                                                                  
                                                                                    console.log("model loaded");
                                                                                  }
                                                                                  document.querySelector('#load').addEventListener('click', test);
                                                                                  
                                                                                  
                                                                                  load model

                                                                                  The message suggest something to do with the Cast operator not being supported by opset 11, maybe you want to use Cast-9. Maybe you have to generate a new model.

                                                                                  Edit

                                                                                  Your model loads using onnxruntime python

                                                                                  sess = onnxruntime.InferenceSession('../../Downloads/onnx_model.onnx');
                                                                                  {i.name: i.shape for i in sess.get_inputs()}
                                                                                  {o.name: o.shape for o in sess.get_outputs()}
                                                                                  
                                                                                  {'input_ids': ['batch', 'sequence'],
                                                                                   'attention_mask': ['batch', 'sequence'],
                                                                                   'token_type_ids': ['batch', 'sequence']}
                                                                                  
                                                                                  {'output_0': ['batch', 2]}
                                                                                  
                                                                                  An Idea

                                                                                  You will probably have to debug it your self, hopefully the only problem is the cast operator.

                                                                                  You start looking here the operator support for onnxjs, and rewrite parts of the model where the operator appears.

                                                                                  For instance, the Cast operator appears only once, you can locate it as follows

                                                                                  import onnx
                                                                                  model = onnx.load('../../Downloads/onnx_model.onnx')
                                                                                  for node in model.graph.node:
                                                                                      if 'cast' in node.op_type.lower():
                                                                                          print(node.name, node.op_type)
                                                                                  

                                                                                  That will print

                                                                                  Cast_2 Cast
                                                                                  

                                                                                  Using https://netron.app/ (Or the desktop version) you can see that it is

                                                                                  So you should simply rewrite how your attention mask is processed in the model, a possible solution would be to let unsqueeze and cast operations outside the model.

                                                                                  Source https://stackoverflow.com/questions/71259717

                                                                                  QUESTION

                                                                                  Spacy NER not recognising NAME
                                                                                  Asked 2022-Mar-03 at 21:37

                                                                                  Can anyone please help me understand why Spacy NER refuses to recognize the last NAME 'Hagrid' in the sentence, no matter the model used (sm, md, lg)?:

                                                                                  "Hermione bought a car, then both Hermione and Hagrid raced it on the track. Tom Brady was very happy with Hagrid this year."

                                                                                  import spacy
                                                                                  nlp = spacy.load('en_core_web_md')
                                                                                  
                                                                                  test_data = "Hermione bought a car, then both Hermione and Hagrid raced it on the track. Tom Brady was very happy with Hagrid this year."
                                                                                  
                                                                                  doc = nlp(test_data)
                                                                                  for ent in doc.ents:
                                                                                          print(ent.text, ent.start_char, ent.end_char, ent.label_)
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2022-Mar-03 at 21:37

                                                                                  Well, Neural Network Models are basically a black box, so there is no way to know this for sure.

                                                                                  I could imagine that the grammar in last sentence is a bit too "fancy"/literature-like if the model was trained on news or web data and might be throwing the model off. This difficulty of seeing the sentence context as something that would be followed up by a name as well as the fact that "Hagrid" is a kind of unusual name could be the reason.

                                                                                  You can try some other models such as the one integrated in Flair:

                                                                                  https://huggingface.co/flair/ner-english-large?text=Hermione+bought+a+car%2C+then+both+Hermione+and+Hagrid+raced+it+on+the+track.+Tom+Brady+was+very+happy+with+Hagrid+this+year.

                                                                                  or this fine-tuned BERT model:

                                                                                  https://huggingface.co/dslim/bert-large-NER?text=Hermione+bought+a+car%2C+then+both+Hermione+and+Hagrid+raced+it+on+the+track.+Tom+Brady+was+very+happy+with+Hagrid+this+year.

                                                                                  They are more powerful and get it right, from my experience SpaCy is a nice tool and quite fast, but not the most precise for NER.

                                                                                  Source https://stackoverflow.com/questions/71340177

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