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Diffstat (limited to 'tensorflow/docs_src/tutorials/word2vec.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/word2vec.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/word2vec.md index 0a1c41c84a..3fe7352bd2 100644 --- a/tensorflow/docs_src/tutorials/word2vec.md +++ b/tensorflow/docs_src/tutorials/word2vec.md @@ -23,7 +23,7 @@ straight in, feel free to look at the minimalistic implementation in This basic example contains the code needed to download some data, train on it a bit and visualize the result. Once you get comfortable with reading and running the basic version, you can graduate to -[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py) +[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py) which is a more serious implementation that showcases some more advanced TensorFlow principles about how to efficiently use threads to move data into a text model, how to checkpoint during training, etc. @@ -341,7 +341,7 @@ t-SNE. Et voila! As expected, words that are similar end up clustering nearby each other. For a more heavyweight implementation of word2vec that showcases more of the advanced features of TensorFlow, see the implementation in -[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). +[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py). ## Evaluating Embeddings: Analogical Reasoning @@ -357,7 +357,7 @@ Download the dataset for this task from To see how we do this evaluation, have a look at the `build_eval_graph()` and `eval()` functions in -[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). +[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py). The choice of hyperparameters can strongly influence the accuracy on this task. To achieve state-of-the-art performance on this task requires training over a @@ -385,13 +385,13 @@ your model is seriously bottlenecked on input data, you may want to implement a custom data reader for your problem, as described in @{$new_data_formats$New Data Formats}. For the case of Skip-Gram modeling, we've actually already done this for you as an example in -[models/tutorials/embedding/word2vec.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec.py). +[models/tutorials/embedding/word2vec.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec.py). If your model is no longer I/O bound but you want still more performance, you can take things further by writing your own TensorFlow Ops, as described in @{$adding_an_op$Adding a New Op}. Again we've provided an example of this for the Skip-Gram case -[models/tutorials/embedding/word2vec_optimized.py](https://github.com/tensorflow/models/tree/master/tutorials/embedding/word2vec_optimized.py). +[models/tutorials/embedding/word2vec_optimized.py](https://www.tensorflow.org/code/tensorflow_models/tutorials/embedding/word2vec_optimized.py). Feel free to benchmark these against each other to measure performance improvements at each stage. |