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February 3, 2022 15:26
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| # wget http://nlp.stanford.edu/data/glove.6B.zip | |
| # unzip glove.6B.zip | |
| import numpy as np | |
| import tensorflow as tf | |
| embeddings_index = {} | |
| with open('glove.6B.100d.txt') as f: | |
| for line in f: | |
| word, coefs = line.split(maxsplit=1) | |
| coefs = np.fromstring(coefs, "f", sep=" ") | |
| embeddings_index[word] = coefs | |
| fixed_embedding_matrix = np.zeros((vocab_size, 100)) | |
| for i, word in enumerate(vocabulary): | |
| embedding_vector = embeddings_index.get(word) | |
| if embedding_vector is not None: | |
| # Words not found in embedding index will be all-zeros. | |
| fixed_embedding_matrix[i] = embedding_vector | |
| fixed_embedding = tf.keras.layers.Embedding( | |
| self.nli_voba_size, | |
| 100, | |
| embeddings_initializer=tf.keras.initializers.Constant(fixed_embedding_matrix), | |
| trainable=False, | |
| mask_zero=True) |
Hi, what is the vocab_size and vocabulary parameter value ?
Hi, what is the vocab_size and vocabulary parameter value ?
Same question. Where did these come from?
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Hi Eileen,
Great work!
This nlp2sql is state of art topics for a longtime. If possible can you create a repository and put all your supporting files there or just share the pretrained model which you mentioned in the blog. Just curious to know how the sql query is generating