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@@ -8,11 +8,11 @@ tags:
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  datasets:
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  - wikipedia
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  model-index:
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- - name: kiddothe2b/hat-mini-1024-LC1
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  results: []
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  ---
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- # Hierarchical Attention Transformer (HAT) / hat-mini-1024-LC1
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  ## Model description
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@@ -35,7 +35,7 @@ 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/hat-mini-1024-I1', trust_remote_code=True)
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  mlm_model("Hello I'm a <mask> model.")
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  ```
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@@ -43,8 +43,8 @@ You can also fine-tun it for SequenceClassification, SequentialSentenceClassific
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  ```python
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  from transformers import AutoTokenizer, AutoModelforSequenceClassification
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- tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hat-mini-1024-I1", trust_remote_code=True)
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- doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hat-base-4096', trust_remote_code=True)
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  ```
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  ## Limitations and bias
 
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  datasets:
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  - wikipedia
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  model-index:
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+ - name: kiddothe2b/hierarchical-transformer-LC1-mini-1024
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  results: []
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  ---
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+ # Hierarchical Attention Transformer (HAT) / hierarchical-transformer-LC1-mini-1024
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  ## Model description
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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-LC1-mini-1024', trust_remote_code=True)
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  mlm_model("Hello I'm a <mask> model.")
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  ```
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  ```python
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  from transformers import AutoTokenizer, AutoModelforSequenceClassification
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+ tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
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+ doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hierarchical-transformer-LC1-mini-1024', trust_remote_code=True)
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  ```
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  ## Limitations and bias