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tags:
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- generated_from_keras_callback
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model-index:
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- name: twitter-roberta-base-hate-multiclass-latest
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# twitter-roberta-base-hate-multiclass-latest
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This model
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It achieves the following results on the evaluation set:
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##
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More information needed
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More information needed
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: None
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- training_precision: float32
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### Training results
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### Framework versions
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- Transformers 4.30.2
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- TensorFlow 2.12.0
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- Datasets 2.10.1
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- Tokenizers 0.12.1
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---
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model-index:
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- name: twitter-roberta-base-hate-multiclass-latest
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results: []
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language:
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- en
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pipeline_tag: text-classification
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# cardiffnlp/twitter-roberta-base-hate-multiclass-latest
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for multiclass hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model.
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## Classes available
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```
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{
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"sexism": 0,
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"racism": 1,
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"disability": 2,
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"sexual_orientation": 3,
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"religion": 4,
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"other": 5,
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"not_hate":6
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}
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```
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## Following metrics are achieved
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* Accuracy: 0.9419
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* Macro-F1: 0.5752
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* Weighted-F1: 0.9390
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### Usage
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Install tweetnlp via pip.
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```shell
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pip install tweetnlp
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```
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Load the model in python.
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```python
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import tweetnlp
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model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
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model.predict('Women are trash 2.')
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>> {'label': 'sexism'}
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model.predict('@user dear mongoloid respect sentiments & belief refrain totalitarianism. @user')
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>> {'label': 'disability'}
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```
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```
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```
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