Update README.md

README.md

@@ -2,7 +2,6 @@
 language: en
 thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true
 tags:
-- exbert
 - huggingtweets
 widget:
 - text: "My dream is"
@@ -19,10 +18,6 @@ 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/l2k&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.
@@ -37,18 +32,18 @@ The model was trained on [@l2k's tweets](https://twitter.com/l2k).
 
 | Data              | Quantity     |
 |-------------------|--------------|
-| Tweets downloaded | 2515    |
-| Retweets          | 562     |
-| Short tweets      | 86 |
-| Tweets kept       | 1867  |
+| Tweets downloaded | 2541    |
+| Retweets          | 578     |
+| Short tweets      | 87 |
+| Tweets kept       | 1876  |
 
-[Explore the data](https://app.wandb.ai/wandb/huggingtweets-dev/runs/3jguvrbl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
+[Explore the data](https://app.wandb.ai/wandb/huggingtweets-dev/runs/18jzfgqc/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 @l2k's tweets.
 
-Hyperparameters and metrics are recorded in the [W&B training run](WANDB_TRAIN) for full transparency and reproducibility.
+Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets-dev/runs/2ly0pm0j) for full transparency and reproducibility.
 
 ## Intended uses & limitations
 
@@ -58,7 +53,8 @@ You can use this model directly with a pipeline for text generation:
 
 ```python
 from transformers import pipeline
-generator = pipeline('text-generation', model='huggingtweets/l2k')
+generator = pipeline('text-generation',
+                     model='huggingtweets/l2k')
 generator("My dream is", max_length=50, num_return_sequences=5)
 ```