model update
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README.md
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@@ -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/twitter-roberta-base-2019-90m-tweetner7-
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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/twitter-roberta-base-2019-90m-tweetner7-
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This model is a fine-tuned version of [tner/twitter-roberta-base-2019-90m-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-2019-90m-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%: [0.6500084574752211, 0.6675327789934176]
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- 95%: [0.6480876172354417, 0.6695072839398589]
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Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-
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and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-
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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/twitter-roberta-base-2019-90m-tweetner7-
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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/twitter-roberta-base-2019-90m-tweetner7-
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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/twitter-roberta-base-2019-90m-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/twitter-roberta-base-2019-90m-tweetner7-continuous
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This model is a fine-tuned version of [tner/twitter-roberta-base-2019-90m-tweetner-2020](https://huggingface.co/tner/twitter-roberta-base-2019-90m-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%: [0.6500084574752211, 0.6675327789934176]
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- 95%: [0.6480876172354417, 0.6695072839398589]
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Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-continuous/raw/main/eval/metric.json)
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and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-2019-90m-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/twitter-roberta-base-2019-90m-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/twitter-roberta-base-2019-90m-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/).
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