Update README.md

README.md

@@ -2,6 +2,7 @@
 license: cc-by-nc-sa-4.0
 pipeline_tag: fill-mask
 language: en
+arxiv: 2210.05529
 tags:
 - long-documents
 datasets:
@@ -15,36 +16,36 @@ 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 miniature BERT [(Turc et al., 2019)](https://arxiv.org/abs/1908.08962), and continued pre-trained for MLM following the paradigm of Longformer released by [Beltagy et al. (2020)](](https://arxiv.org/abs/1908.08962)). It supports sequences of length up to 1,024.
+The model has been warm-started re-using the weights of miniature BERT (Turc et al., 2019), and continued pre-trained for MLM following the paradigm of Longformer released by Beltagy et al. (2020). It supports sequences of length up to 1,024.
 
-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-I3-mini-1024", trust_remote_code=True)
-mlm_model = AutoModelforForMaskedLM(model='kiddothe2b/hierarchical-transformer-I3-mini-1024', trust_remote_code=True)
+mlm_model = AutoModelforForMaskedLM("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
 
 ```
 
-You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
+You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
 
 ```python
 from transformers import AutoTokenizer, AutoModelforSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
-doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hierarchical-transformer-I3-mini-1024', trust_remote_code=True)
+doc_classifier = AutoModelforSequenceClassification("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
 ```
 
 ## Limitations and bias
@@ -99,11 +100,13 @@ 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). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
 
 ```
 @misc{chalkidis-etal-2022-hat,
-  url = {https://arxiv.org/abs/xxx},
+  url = {https://arxiv.org/abs/2210.05529},
   author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
   title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
   publisher = {arXiv},
@@ -112,3 +115,4 @@ If you use HAT in your research, please cite [An Exploration of Hierarchical Att
 ```
 
 
+