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model update

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  1. README.md +24 -4
README.md CHANGED
@@ -73,7 +73,7 @@ model-index:
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  pipeline_tag: token-classification
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  widget:
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- - text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
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  example_title: "NER Example 1"
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  ---
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  # tner/twitter-roberta-base-dec2021-tweetner7-2021
@@ -112,15 +112,34 @@ Full evaluation can be found at [metric file of NER](https://huggingface.co/tner
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  and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2021/raw/main/eval/metric_span.json).
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  ### Usage
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- This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
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  ```shell
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  pip install tner
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  ```
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- and activate model as below.
 
 
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  ```python
 
 
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  from tner import TransformersNER
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  model = TransformersNER("tner/twitter-roberta-base-dec2021-tweetner7-2021")
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- model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
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  ```
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  It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
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@@ -166,3 +185,4 @@ If you use any resource from T-NER, please consider to cite our [paper](https://
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  }
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  ```
 
 
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  pipeline_tag: token-classification
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  widget:
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+ - text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
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  example_title: "NER Example 1"
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  ---
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  # tner/twitter-roberta-base-dec2021-tweetner7-2021
 
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  and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2021/raw/main/eval/metric_span.json).
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  ### Usage
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+ This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip.
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  ```shell
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  pip install tner
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  ```
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+ [TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
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+ converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
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+
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  ```python
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+ import re
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+ from urlextract import URLExtract
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  from tner import TransformersNER
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+
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+ extractor = URLExtract()
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+
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+ def format_tweet(tweet):
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+ # mask web urls
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+ urls = extractor.find_urls(tweet)
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+ for url in urls:
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+ tweet = tweet.replace(url, "{{URL}}")
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+ # format twitter account
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+ tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
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+ return tweet
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+
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+
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+ text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
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+ text_format = format_tweet(text)
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  model = TransformersNER("tner/twitter-roberta-base-dec2021-tweetner7-2021")
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+ model.predict([text_format])
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  ```
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  It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
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  }
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  ```
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+