Update README.md

README.md

@@ -1,7 +1,9 @@
 ---
 license: cc-by-nc-sa-4.0
 pipeline_tag: fill-mask
+arxiv: 2210.05529
 language: en
+thumbnail: https://raw.githubusercontent.com/coastalcph/hierarchical-transformers/main/data/figures/hat_encoder.png
 tags:
 - long-documents
 datasets:
@@ -15,28 +17,27 @@ model-index:
 
 ## Model description
 
-This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/xxx). 
+This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529). 
 
 The model has been warm-started re-using the weights of RoBERTa [(Liu et al., 2019)](https://arxiv.org/abs/1907.11692), and continued pre-trained for MLM in long sequences following the paradigm of Longformer released by [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150). It supports sequences of length up to 4,096.
 
-HAT use a hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think segments as paragraphs or sentences.
+HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
 
 ## Intended uses & limitations
 
 You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
-See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for fine-tuned versions on a task that
-interests you.
+See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT or fine-tuned versions on a task that interests you.
 
-Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification or question answering.
+Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
 
 ## How to use
 
-You can use this model directly with a pipeline for masked language modeling:
+You can use this model directly for masked language modeling:
 
 ```python
 from transformers import AutoTokenizer, AutoModelforForMaskedLM
 tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
-mlm_model = AutoModelforForMaskedLM(model='kiddothe2b/hierarchical-transformer-base-4096', trust_remote_code=True)
+mlm_model = AutoModelforForMaskedLM("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
 ```
 
 You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
@@ -44,7 +45,7 @@ You can also fine-tun it for SequenceClassification, SequentialSentenceClassific
 ```python
 from transformers import AutoTokenizer, AutoModelforSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
-doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hierarchical-transformer-base-4096', trust_remote_code=True)
+doc_classifier = AutoModelforSequenceClassification("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
 ```
 
 ## Limitations and bias
@@ -98,7 +99,7 @@ The following hyperparameters were used during training:
 
 ## Citing
 
-If you use HAT in your research, please cite [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/xxx)
+If you use HAT in your research, please cite [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529)
 
 ```
 @misc{chalkidis-etal-2022-hat,