Update README.md

README.md

@@ -2,14 +2,24 @@
 language: en
 thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true
 tags:
-- exbert
 - huggingtweets
 widget:
 - text: "My dream is"
 ---
 
+<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
+
+<style>
+@media (prefers-color-scheme: dark) {
+  .prose { color: #E2E8F0 !important; }
+  .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
+}
+</style>
+
+<section class='prose'>
+
 <div>
-<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1278494692053618688/FEsN6_IF_400x400.jpg')">
+<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/1302839376909488128/fPooODvu_400x400.jpg')">
 </div>
 <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lavanya 🤖 AI Bot </div>
 <div style="font-size: 15px; color: #657786">@lavanyaai bot</div>
@@ -19,10 +29,6 @@ I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
 
 Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
 
-<a href="https://huggingface.co/exbert/?model=huggingtweets/lavanyaai&modelKind=autoregressive&sentence=I%20love%20huggingtweets!&layer=11">
-	<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
-</a>
-
 ## How does it work?
 
 The model uses the following pipeline.
@@ -35,34 +41,56 @@ To understand how the model was developed, check the [W&B report](https://bit.ly
 
 The model was trained on [@lavanyaai's tweets](https://twitter.com/lavanyaai).
 
-| Data              | Quantity     |
-|-------------------|--------------|
-| Tweets downloaded | 3229    |
-| Retweets          | 1570     |
-| Short tweets      | 147 |
-| Tweets kept       | 1512  |
-
-[Explore the data](https://app.wandb.ai/borisd13/huggingtweets/runs/33pvebva/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
+<table style='border-width:0'>
+<thead style='border-width:0'>
+<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
+<th style='border-width:0'>Data</th>
+<th style='border-width:0'>Quantity</th>
+</tr>
+</thead>
+<tbody style='border-width:0'>
+<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
+<td style='border-width:0'>Tweets downloaded</td>
+<td style='border-width:0'>3185</td>
+</tr>
+<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
+<td style='border-width:0'>Retweets</td>
+<td style='border-width:0'>1489</td>
+</tr>
+<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
+<td style='border-width:0'>Short tweets</td>
+<td style='border-width:0'>218</td>
+</tr>
+<tr style='border-width:0'>
+<td style='border-width:0'>Tweets kept</td>
+<td style='border-width:0'>1478</td>
+</tr>
+</tbody>
+</table>
+
+[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2yz2yktg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
 
 ## Training procedure
 
-The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lavanyaai's tweets for 4 epochs.
+The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lavanyaai's tweets.
+
+Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1bnzmvte) for full transparency and reproducibility.
 
-Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/borisd13/huggingtweets/runs/2paux5t9).
+At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1bnzmvte/artifacts) is logged and versioned.
 
 ## Intended uses & limitations
 
-#### How to use
+### How to use
 
 You can use this model directly with a pipeline for text generation:
 
-```python
-from transformers import pipeline
-generator = pipeline('text-generation', model='huggingtweets/lavanyaai')
-generator("My dream is", max_length=50, num_return_sequences=5)
-```
+<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
+generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
+                     model=<span style="color:#FF9800">'huggingtweets/lavanyaai'</span>)
+generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
 
-#### Limitations and bias
+
+### Limitations and bias
 
 The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
 
@@ -72,8 +100,12 @@ In addition, the data present in the user's tweets further affects the text gene
 
 *Built by Boris Dayma*
 
+</section>
+
 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/borisdayma)
 
+<section class='prose'>
 For more details, visit the project repository.
+</section>
 
 [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)