comet links fix

Author: vpj

docs/transformers/basic/autoregressive_experiment.html

@@ -70,7 +70,7 @@
                 <a href='#section-0'>#</a>
             </div>
             <h1>Transformer Auto-Regression Experiment</h1>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This trains a simple transformer introduced in <a href="https://papers.labml.ai/paper/1706.03762">Attention Is All You Need</a> on an NLP auto-regression task (with Tiny Shakespeare dataset).</p>
 
         </div>

docs/transformers/mha.html

@@ -69,7 +69,7 @@
                 <a href='#section-0'>#</a>
             </div>
             <h1>Multi-Headed Attention (MHA)</h1>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 <p>This is a tutorial/implementation of multi-headed attention from paper <a href="https://papers.labml.ai/paper/1706.03762">Attention Is All You Need</a> in <a href="https://pytorch.org/">PyTorch</a>. The implementation is inspired from <a href="https://nlp.seas.harvard.edu/2018/04/03/attention.html">Annotated Transformer</a>.</p>
 <p>Here is the <a href="basic/autoregressive_experiment.html">training code</a> that uses a basic transformer with MHA for NLP auto-regression.</p>
 <p><a href="basic/autoregressive_experiment.html">Here is an experiment implementation</a> that trains a simple transformer.</p>

docs/transformers/models.html

@@ -69,7 +69,7 @@
                 <a href='#section-0'>#</a>
             </div>
             <h1>Transformer Encoder and Decoder Models</h1>
-<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082"><img alt="Open In Comet" src="https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model"></a></p>
 
         </div>
         <div class='code'>

labml_nn/transformers/basic/autoregressive_experiment.ipynb

@@ -11,7 +11,7 @@
    "source": [
     "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb)\n",
-    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)\n",
+    "[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)\n",
     "\n",
     "## Transformer Experiment\n",
     "\n",

labml_nn/transformers/basic/autoregressive_experiment.py

@@ -8,7 +8,7 @@
 # Transformer Auto-Regression Experiment
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)
 
 This trains a simple transformer introduced in [Attention Is All You Need](https://papers.labml.ai/paper/1706.03762)
 on an NLP auto-regression task (with Tiny Shakespeare dataset).

labml_nn/transformers/mha.py

@@ -9,7 +9,7 @@
 # Multi-Headed Attention (MHA)
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)
 
 This is a tutorial/implementation of multi-headed attention
 from paper [Attention Is All You Need](https://papers.labml.ai/paper/1706.03762)

labml_nn/transformers/models.py

@@ -9,7 +9,7 @@
 # Transformer Encoder and Decoder Models
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/basic/autoregressive_experiment.ipynb)
-[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)
+[![Open In Comet](https://images.labml.ai/images/comet.svg?experiment=capsule_networks&file=model)](https://comet.ml/labml/transformer/ea8c108c2d94434ca3c2bc2b21015082)
 """
 import math