Comments (1)
guillaumegenthial
commented on May 18, 2024
1
- For the dataset, you'll just need to keep the data format described in the README (one line -> one word and its tag, new sentence = empty line). Optionally, you can change the way the class Dataset works.
- For the word vectors, for now only GloVe is supported, but you could easily write some code to load the Word2Vec vectors. You would only need to adapt the export_trimmed_glove_vectors functions defined in data_utils.py. This function takes the name of the file of word vectors, the vocab (word -> id) and exports a np array E such that E[id] = the word vector. There should be a lot of code available online to perform such a task. You can have a look at gensim.
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Related Issues (20)
- recipe for target 'run' failed HOT 4
- Unicode Decode Error HOT 2
- Prediction shows I-LOC without B-LOC HOT 2
- how to shuffle batches with the iterator? HOT 1
- The loss depends on the max length of sequence in a batch, which should not be the case. HOT 1
- Extraction of specific expressions HOT 2
- Char Embedding Error HOT 2
- Huge error on prediction (NER) HOT 1
- how to get the input and output nodes of the model HOT 1
- How to get or create 'data/glove.6B/glove.6B.300d.txt'? HOT 6
- How to improve precision by this model?
- Not mentioned <start>、<end> token
- How to evaluate the precision, recall and F1-value of the code?
- KeyError: 'O' makefile:7: recipe for target 'run' failed HOT 1
- what is the difference between self.word_embeddings with tf.nn.dropout and without it
- Saving the model weights in TF 2
- F1 score of all custom named entity
- How can I get eng.testa.iob?
- Why use training, validation and test set to build vocabulary in build_data.py ? Isn't it information leak ?
- Code snippet for tensorflow 2.x
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from sequence_tagging.