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model update

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  1. README.md +6 -6
README.md CHANGED
@@ -6,7 +6,7 @@ metrics:
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  - precision
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  - recall
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  model-index:
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- - name: tner/bert-large-tweetner7-2020-2021-continuous
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  results:
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  - task:
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  name: Token Classification
@@ -76,7 +76,7 @@ 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/bert-large-tweetner7-2020-2021-continuous
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  This model is a fine-tuned version of [tner/bert-large-tweetner-2020](https://huggingface.co/tner/bert-large-tweetner-2020) on the
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  [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on `train_2021`.
@@ -108,8 +108,8 @@ For F1 scores, the confidence interval is obtained by bootstrap as below:
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  - 90%: [0.6231013705127983, 0.6413574593408826]
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  - 95%: [0.6217502353949177, 0.6428942705896876]
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- Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-large-tweetner7-2020-2021-continuous/raw/main/eval/metric.json)
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- and [metric file of entity span](https://huggingface.co/tner/bert-large-tweetner7-2020-2021-continuous/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
@@ -119,7 +119,7 @@ pip install tner
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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/bert-large-tweetner7-2020-2021-continuous")
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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.
@@ -143,7 +143,7 @@ The following hyperparameters were used during training:
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  - lr_warmup_step_ratio: 0.3
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  - max_grad_norm: 1
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- The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-large-tweetner7-2020-2021-continuous/raw/main/trainer_config.json).
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  ### Reference
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  If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
 
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  - precision
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  - recall
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  model-index:
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+ - name: tner/bert-large-tweetner7-continuous
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  results:
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  - task:
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  name: Token Classification
 
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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/bert-large-tweetner7-continuous
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  This model is a fine-tuned version of [tner/bert-large-tweetner-2020](https://huggingface.co/tner/bert-large-tweetner-2020) on the
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  [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on `train_2021`.
 
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  - 90%: [0.6231013705127983, 0.6413574593408826]
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  - 95%: [0.6217502353949177, 0.6428942705896876]
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+ Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-large-tweetner7-continuous/raw/main/eval/metric.json)
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+ and [metric file of entity span](https://huggingface.co/tner/bert-large-tweetner7-continuous/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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  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/bert-large-tweetner7-continuous")
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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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  - lr_warmup_step_ratio: 0.3
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  - max_grad_norm: 1
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+ The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-large-tweetner7-continuous/raw/main/trainer_config.json).
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  ### Reference
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  If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).