Update README.md

README.md

@@ -8,11 +8,11 @@ tags:
 datasets:
 - wikipedia
 model-index:
-- name: kiddothe2b/hat-mini-1024-I2
+- name: kiddothe2b/hierarchical-transformer-I3-mini-1024
   results: []
 ---
 
-# Hierarchical Attention Transformer (HAT) / hat-mini-1024-I2
+# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-I3-mini-1024
 
 ## Model description
 
@@ -25,7 +25,7 @@ HAT use a hierarchical attention, which is a combination of segment-wise and cro
 ## 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?other=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 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.
 
@@ -35,7 +35,7 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 from transformers import pipeline
-mlm_model = pipeline('fill-mask', model='kiddothe2b/hat-mini-1024-I1', trust_remote_code=True)
+mlm_model = pipeline('fill-mask', model='kiddothe2b/hierarchical-transformer-I3-mini-1024', trust_remote_code=True)
 mlm_model("Hello I'm a <mask> model.")
 ```
 
@@ -43,8 +43,8 @@ You can also fine-tun it for SequenceClassification, SequentialSentenceClassific
 
 ```python
 from transformers import AutoTokenizer, AutoModelforSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hat-mini-1024-I1", trust_remote_code=True)
-doc_classifier = AutoModelforSequenceClassification(model='kiddothe2b/hat-base-4096', trust_remote_code=True)
+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)
 ```
 
 ## Limitations and bias