model update

README.md

@@ -6,7 +6,7 @@ metrics:
 - precision
 - recall
 model-index:
-- name: tner/roberta-large-tweetner7-2020-selflabel2020-continuous
+- name: tner/roberta-large-tweetner7-selflabel2020-continuous
   results:
   - task:
       name: Token Classification
@@ -76,7 +76,7 @@ widget:
 - text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
   example_title: "NER Example 1"
 ---
-# tner/roberta-large-tweetner7-2020-selflabel2020-continuous
+# tner/roberta-large-tweetner7-selflabel2020-continuous
 
 This model is a fine-tuned version of [tner/roberta-large-tweetner-2020](https://huggingface.co/tner/roberta-large-tweetner-2020) on the 
 [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2020` split of the [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) annotated by [tner/roberta-large](https://huggingface.co/tner/roberta-large-tweetner7-2020)). Please check [https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling) for more detail of reproducing the model.  The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on the self-labeled dataset. 
@@ -108,8 +108,8 @@ For F1 scores, the confidence interval is obtained by bootstrap as below:
     - 90%: [0.6429569959405362, 0.6605302879870334]
     - 95%: [0.6410815271146394, 0.6628490227012314] 
 
-Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2020-continuous/raw/main/eval/metric.json) 
-and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2020-continuous/raw/main/eval/metric_span.json).
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous/raw/main/eval/metric_span.json).
 
 ### Usage
 This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip   
@@ -119,7 +119,7 @@ pip install tner
 and activate model as below.
 ```python
 from tner import TransformersNER
-model = TransformersNER("tner/roberta-large-tweetner7-2020-selflabel2020-continuous")
+model = TransformersNER("tner/roberta-large-tweetner7-selflabel2020-continuous")
 model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
 ```
 It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
@@ -143,7 +143,7 @@ The following hyperparameters were used during training:
  - lr_warmup_step_ratio: 0.3
  - max_grad_norm: 1
 
-The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2020-continuous/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous/raw/main/trainer_config.json).
 
 ### Reference
 If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).