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import gradio as gr |
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from transformers import TFBertModel, TFXLMRobertaModel |
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import numpy as np |
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import tensorflow as tf |
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from transformers import AutoTokenizer |
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from huggingface_hub import from_pretrained_keras |
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app_title = "Portuguese Counter Hate Speech Detection" |
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app_description = """ |
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This prototype from the kNOwHATE project aims to classify a Portuguese target sentence as either hate speech, counter hate speech or neutral, considering another sentence as context. |
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We collected 24,739 YouTube comments and 29,846 tweets, annotated by experts, and trained our prototype on this data. |
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We invite you to try it out. You can just enter a pair of sentences below, one as target and another as context, and submit it to see if the target is either hate speech, counter hate speech or neutral, relative to the context. |
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For more, visit our [website](https://knowhate.eu) and [Hugging Face page](https://huggingface.co/knowhate). |
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""" |
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def_model = 'knowhate/counterhate-twitter-bertimbau' |
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model_list = [ |
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def_model, |
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"knowhate/counterhate-twitter-xlmrobertabase", |
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"knowhate/counterhate-twitter-bertbasemultilingualcased", |
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"knowhate/counterhate-twitter-hateberttuga", |
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"knowhate/counterhate-youtube-hateberttuga", |
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"knowhate/counterhate-youtube-bertimbau" |
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] |
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kw_to_hf = {"knowhate/counterhate-twitter-bertimbau": "neuralmind/bert-base-portuguese-cased", |
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"knowhate/counterhate-youtube-bertimbau": "neuralmind/bert-base-portuguese-cased", |
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"knowhate/counterhate-twitter-xlmrobertabase": "xlm-roberta-base", |
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"knowhate/counterhate-twitter-bertbasemultilingualcased": "bert-base-multilingual-cased", |
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"knowhate/counterhate-youtube-hateberttuga": "knowhate/hateberttuga", |
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"knowhate/counterhate-twitter-hateberttuga": "knowhate/hateberttuga"} |
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app_examples = [ |
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["Essa gente tem é de deixar de ser apaparicada pelo Estado e começar a cumprir os seus deveres como cidadãos", |
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"Nepia o que faz com que as pessoas generalizem é o ódio intrínseco que têm contra uma etnia, ng é responsável pela sua xenofobia", |
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def_model], |
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["Nem vou comentar o hate e misoginia que tenho visto aqui no tt em relação à Anitta", |
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"E xenofobia também. Tugas no seu melhor", |
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def_model], |
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["A Festa tá no Climax, chama o zuca pra Dançar.", |
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"Já reparaste no contador da luz? Vai trabalhar malandro", |
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def_model] |
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] |
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def predict(text, target, chosen_model): |
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model1 = from_pretrained_keras(chosen_model) |
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checkpoint = kw_to_hf[chosen_model] |
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if '/' in checkpoint: |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=True, model_max_length=512) |
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else: |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=True) |
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tokpair = tokenizer(text, target, truncation=True, padding='max_length', return_tensors='tf', return_token_type_ids=False) |
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outp = model1.signatures["serving_default"](**tokpair) |
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proto_tensor = tf.make_tensor_proto(outp['outp']) |
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allscores = tf.make_ndarray(proto_tensor)[0] |
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scores_dict = { |
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'Neutral': allscores[0], |
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'Counter Speech': allscores[1], |
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'Hate Speech': allscores[2] |
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} |
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return scores_dict |
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inputs = [ |
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gr.Textbox(label="Context", value= app_examples[0][0]), |
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gr.Textbox(label="Target", value= app_examples[0][1]), |
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gr.Dropdown(label="Model", choices=model_list, value=model_list[0]) |
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] |
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outputs = [ |
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gr.Label(label="Result"), |
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] |
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gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title=app_title, |
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description=app_description, examples=app_examples, theme=gr.themes.Base(primary_hue="red")).launch() |
