New model from https://wandb.ai/wandb/huggingtweets/runs/16ugp9hy

README.md

@@ -1,28 +1,17 @@
 ---
 language: en
-thumbnail: https://www.huggingtweets.com/naval/1611300289258/predictions.png
+thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
 tags:
 - 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/1256841238298292232/ycqwaMI2_400x400.jpg')">
 </div>
 <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Naval 🤖 AI Bot </div>
-<div style="font-size: 15px; color: #657786">@naval bot</div>
+<div style="font-size: 15px">@naval bot</div>
 </div>
 
 I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
@@ -35,62 +24,41 @@ The model uses the following pipeline.
 
 ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true)
 
-To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
+To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
 
 ## Training data
 
 The model was trained on [@naval's tweets](https://twitter.com/naval).
 
-<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'>3233</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'>128</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'>660</td>
-</tr>
-<tr style='border-width:0'>
-<td style='border-width:0'>Tweets kept</td>
-<td style='border-width:0'>2445</td>
-</tr>
-</tbody>
-</table>
-
-[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/216kkfw1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
+| Data | Quantity |
+| --- | --- |
+| Tweets downloaded | 3250 |
+| Retweets | 138 |
+| Short tweets | 657 |
+| Tweets kept | 2455 |
+
+[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p7s0zjvq/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 @naval's tweets.
 
-Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/czon8gpc) for full transparency and reproducibility.
+Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16ugp9hy) for full transparency and reproducibility.
 
-At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/czon8gpc/artifacts) is logged and versioned.
+At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16ugp9hy/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:
 
-<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/naval'</span>)
-generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
-
+```python
+from transformers import pipeline
+generator = pipeline('text-generation',
+                     model='huggingtweets/naval')
+generator("My dream is", num_return_sequences=5)
+```
 
-### 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).
 
@@ -100,12 +68,8 @@ 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/intent/follow?screen_name=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)

config.json

@@ -34,7 +34,7 @@
       "top_p": 0.95
     }
   },
-  "transformers_version": "4.2.2",
+  "transformers_version": "4.5.0",
   "use_cache": true,
   "vocab_size": 50257
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
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tokenizer_config.json

@@ -1 +1 @@
-{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "name_or_path": "gpt2"}
+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2"}

training_args.bin

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+size 2351