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  ---
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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 was trained from scratch on an unknown dataset.
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- It achieves the following results on the evaluation set:
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- ## Model description
 
 
 
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- More information needed
 
 
 
 
 
 
 
 
 
 
 
 
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- ## Intended uses & limitations
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- More information needed
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- ## Training and evaluation data
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- More information needed
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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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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-
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- ### Training results
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-
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-
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-
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- ### Framework versions
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-
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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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  ---
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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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+ ```