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@@ -3,7 +3,7 @@ license: cc-by-nc-sa-4.0
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  pipeline_tag: fill-mask
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  language: en
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  tags:
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- - long_documents
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  datasets:
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  - c4
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  model-index:
@@ -34,9 +34,9 @@ Note that this model is primarily aimed at being fine-tuned on tasks that use th
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  You can use this model directly with a pipeline for masked language modeling:
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  ```python
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- from transformers import pipeline
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- mlm_model = pipeline('fill-mask', model='kiddothe2b/hierarchical-transformer-base-4096', trust_remote_code=True)
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- mlm_model("Hello I'm a <mask> model.")
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  ```
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  You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
@@ -96,7 +96,8 @@ The following hyperparameters were used during training:
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  - Tokenizers 0.11.6
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- ##Citing
 
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  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)
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  ```
 
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  pipeline_tag: fill-mask
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  language: en
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  tags:
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+ - long-documents
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  datasets:
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  - c4
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  model-index:
 
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  You can use this model directly with a pipeline for masked language modeling:
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  ```python
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+ from transformers import AutoTokenizer, AutoModelforForMaskedLM
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+ tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
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+ mlm_model = AutoModelforForMaskedLM(model='kiddothe2b/hierarchical-transformer-base-4096', trust_remote_code=True)
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  ```
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  You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
 
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  - Tokenizers 0.11.6
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+ ## Citing
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+
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  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)
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  ```