GloVe | GloVe model for distributed word representation | Natural Language Processing library

 by   stanfordnlp C Version: 1.2 License: Apache-2.0

kandi X-RAY | GloVe Summary

kandi X-RAY | GloVe Summary

GloVe is a C library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Bert applications. GloVe has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

We provide an implementation of the GloVe model for learning word representations, and describe how to download web-dataset vectors or train your own. See the project page or the paper for more information on glove vectors.

            kandi-support Support

              GloVe has a medium active ecosystem.
              It has 6366 star(s) with 1455 fork(s). There are 228 watchers for this library.
              It had no major release in the last 12 months.
              There are 78 open issues and 79 have been closed. On average issues are closed in 232 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of GloVe is 1.2

            kandi-Quality Quality

              GloVe has 0 bugs and 0 code smells.

            kandi-Security Security

              GloVe has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              GloVe code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              GloVe is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              GloVe releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              It has 84 lines of code, 2 functions and 1 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            GloVe Key Features

            No Key Features are available at this moment for GloVe.

            GloVe Examples and Code Snippets

            Example of the Glove model .
            pythondot img1Lines of Code : 21dot img1no licencesLicense : No License
            copy iconCopy
            def main(we_file='glove_model_50.npz', w2i_file='glove_word2idx_50.json'):
                words = ['japan', 'japanese', 'england', 'english', 'australia', 'australian', 'china', 'chinese', 'italy', 'italian', 'french', 'france', 'spain', 'spanish']
                with op  
            Extract embedding vectors from GloVe .
            pythondot img2Lines of Code : 17dot img2License : Permissive (MIT License)
            copy iconCopy
            def get_embedding_vectors(tokenizer, dim=100):
                embedding_index = {}
                with open(f"data/glove.6B.{dim}d.txt", encoding='utf8') as f:
                    for line in tqdm.tqdm(f, "Reading GloVe"):
                        values = line.split()
                        word = values[0  

            Community Discussions


            Is storing an object as a value in a key:value pair safe in Python?
            Asked 2022-Mar-31 at 04:17

            I'm designing the mechanics behind a RPG. There are classes for Item, Player, NPC, etc. The Player class has attributes inventory and equipment. Equipment is a list of dictionaries, such as:



            Answered 2022-Mar-31 at 04:17

            Is it safe, efficient, and reliable to pass an entire object as a value?

            Yes! Everything in Python is an object.

            If I'm correct, this is the print function returning an address in memory denoting the object. Does this represent any issues? ... is the print function returning an address in memory denoting the object. Does this represent any issues?

            No issues here. It depends entirely on the __repr__ overrides of the class. If it doesn't have one, then the default implementation is to print out the id() of the object and its class type. E.g.



            tf2.0: Gradient Tape returns None gradient in RNN model
            Asked 2022-Mar-27 at 23:56

            In a model with an embedding layer and SimpleRNN layer, I would like to compute the partial derivative dh_t/dh_0 for each step t.

            The structure of my model, including imports and data preprocessing.
            Toxic comment train data available:
            GloVe 6B 100d embeddings available:



            Answered 2022-Feb-18 at 14:02

            You could maybe try using tf.gradients. Also rather use tf.Variable for h0:



            Error while loading vector from Glove in Spacy
            Asked 2022-Mar-17 at 16:39

            I am facing the following attribute error when loading glove model:

            Code used to load model:



            Answered 2022-Mar-17 at 14:08

            spacy version: 3.1.4 does not have the feature from_glove.

            I was able to use nlp.vocab.vectors.from_glove() in spacy version: 2.2.4.

            If you want, you can change your spacy version by using:

            !pip install spacy==2.2.4 on your Jupyter cell.



            Difference in hand color between pretrain dataset and fine dataset?
            Asked 2022-Jan-22 at 07:37

            I have a pose estimation model pretrained on a dataset in which hands are in its nartural color. I want to finetune that model on the dataset of hands of surgeons doing surgeries. Those hands are in surgical gloves so the image of the hands are a bit different than normal hands.

            pretraine image

            finetune image

            Does this difference in hand colors affect the model performance? If I can make images of those surgical hands more like normal hands, will I get better performance?



            Answered 2022-Jan-22 at 07:37

            Well, it depends on what your pre-trained model has learned to capture from the pre-training (initial) dataset. Suppose your model had many feature maps and not enough skin color variation in your pre-training dataset (leads to overfitting issues). In that case, your model has likely "taken the path of least resistance" and exploited that to learn feature maps that rely on the color space as means of feature extraction (which might not generalize well due to color differences).

            The more your pre-training dataset match/overlap with your target dataset, the better the effects of transfer learning will be. So yes, there is a very high chance that making your target dataset (surgical hands) look more similar to your pre-training dataset will positively impact your model's performance. Moreover, I would conjecture that introducing some color variation (e.g., Color Jitter augmentation) in your pre-training dataset could also help your model generalize to your target dataset.



            How does text encoding from tensorflow.keras.preprocessing.text.Tokenizer differ from the old tfds.deprecated.text.TokenTextEncoder
            Asked 2021-Dec-23 at 07:49

            In the deprecated encoding method with tfds.deprecated.text.TokenTextEncoder We first create a vocab set of token



            Answered 2021-Dec-22 at 09:53

            Maybe try something like this:



            Pull out categorical rows and apply to all subsequent rows until new category found
            Asked 2021-Oct-13 at 18:29

            Here is a dummy DataFrame of my data, I have categorical rows (represented by the existence of NaN value of 'Price') and data rows (represented by a non-NaN value of 'Price').



            Answered 2021-Oct-13 at 18:17

            Try mask the notna, then ffill to get the correct Sport:



            How to apply a function for each row in the column python
            Asked 2021-Sep-08 at 09:17

            I have a code like this



            Answered 2021-Sep-03 at 19:41

            I ran the code, but as I don't have the necessary tokenizer packages installed, I couldnt get that to run. Instead, I ran a simpler function below:



            xpath selector returns empty values
            Asked 2021-Aug-23 at 17:19

            i'm trying to manipulate text from all spans under "critical-product-marquee-container" div using python, selenium and xpath selector.



            Answered 2021-Aug-23 at 09:15

            basically that's marquee in HTML5, so you have to explicitly wait for each elements.

            Code :



            Validation accuracy is much less than Training accuracy
            Asked 2021-Aug-20 at 08:25

            I am using MOSI dataset for the multimodal sentiment analysis, where for now I am training the model for text dataset only. For text, I am using glove embeddings of 300 dimensions for processing text. My total vocab size is 2173 and my padded sequence length is 30. My target array is [0,0,0,0,0,0,1] where left most is highly -ve and right most highly +ve.

            I am splitting the dataset like this

            X_train, X_test, y_train, y_test = train_test_split(WDatasetX, y7, test_size=0.20, random_state=42)

            My tokenization process is



            Answered 2021-Aug-11 at 19:22

            A large difference between Train and Validation stats typically indicates overfitting of models to the Train data.

            To minimize this I do a few things

            1. reduce the size of the model.
            2. Add a few dropout or similar layers in the model. I have had good success with using these layers: layers.LeakyReLU(alpha=0.8),

            See guidance here:



            AttributeError: 'Word2Vec' object has no attribute 'most_similar' (Word2Vec)
            Asked 2021-Aug-06 at 19:59

            I am using Word2Vec and using a wiki trained model that gives out the most similar words. I ran this before and it worked but now it gives me this error even after rerunning the whole program. I tried to take off return_path=True but im still getting the same error



            Answered 2021-Aug-06 at 18:44

            You are probably looking for .wv.most_similar, so please try:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install GloVe

            The links below contain word vectors obtained from the respective corpora. If you want word vectors trained on massive web datasets, you need only download one of these text files! Pre-trained word vectors are made available under the Public Domain Dedication and License.
            Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors, 1.75 GB download): [mirror]
            Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): [mirror]
            Wikipedia 2014 + Gigaword 5 (6B tokens, 400K vocab, uncased, 300d vectors, 822 MB download): [mirror]
            Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased, 200d vectors, 1.42 GB download): [mirror]


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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