skip-thoughts
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ryankiros
Sent2Vec encoder and training code from the paper "Skip-Thought Vectors"
if i have many sentence, i want to get the sentence vectors, what should i do?
size of my vocab is 175.But why is it looking for 176? Error: model = tools.load_model(embed_map) File "/home/raksha/prog/skip-thoughts-master/training/tools.py", line 74, in load_model table = lookup_table(options, embed_map, worddict, word_idict, f_emb) File...
When I run the training Step 4: Loading saved models Errors: In [10]: model = tools.load_model(embed_map) Loading dictionary... Creating inverted dictionary... Loading model options... Loading model parameters... Compiling encoder... /usr/lib/python2.7/site-packages/theano/scan_module/scan.py:1019:...
I spent hours solving issue #50 because I misinterpreted step 4. So updated README.md for decoding directory.
import vocab Traceback (most recent call last): File "", line 1, in import vocab ImportError: No module named vocab >>>
- Added setup.py so that can be installed by pip - Made the path to tables and models configurable from `load_model`, with sensible defaults - Adjusted readme - Added **init**.py
Dear experts, I recently stumbled upon the Skip-Thought paper and found it extremely interesting. I have managed to train a small model using some 2.7 million sentences for testing purposes....
A type error occurs at the following line while running the evaluation code eval_sick.py: >> bestlrmodel = copy.deepcopy(lrmodel) TypeError: 'cannot deepcopy this pattern object' Any quick fix?
The arguments of Dense should be `Dense(nclass, input_dim=ninputs)` instead of `Dense(ninputs, nclass)`
