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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- counter speech |
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base_model: openai-community/gpt2-medium |
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--- |
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--- |
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# Target-Aware Counter-Speech Generation |
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<!-- Provide a quick summary of what the model is/does. --> |
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The target-aware counter-speech generation model is an autoregressive generative language model fine-tuned on hate- and counter-speech pairs from the [CONAN](https://github.com/marcoguerini/CONAN) datasets for generating more contextually relevant counter-speech, based on the [gpt2-medium](https://huggingface.co./gpt2-medium) model. |
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The model utilizes special tokens that embedded target demographic information to guide the generation towards more relevant responses, avoiding off-topic and generic responses. The model is trained on 8 target demographics, including Migrants, People of Color (POC), LGBT+, Muslims, Women, Jews, Disabled, and Other. |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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The model is intended for generating counter-speech responses for a given hate speech sequence, combined with special tokens for target-demographic embeddings. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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We observed negative effects such as content hallucination and toxic response generation. Though the intended use is to generate counter-speech for combating online hatred, the usage is to be monitored carefully with human post-editing or approval system, ensuring safe and inclusive online environment. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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types = ["MIGRANTS", "POC", "LGBT+", "MUSLIMS", "WOMEN", "JEWS", "other", "DISABLED"] # A list of all available target-demographic tokens |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained(tum-nlp/gpt-2-medium-target-aware-counterspeech-generation) |
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tokenizer = AutoTokenizer.from_pretrained(tum-nlp/gpt-2-medium-target-aware-counterspeech-generation) |
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tokenizer.padding_side = "left" |
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prompt = "<|endoftext|> <other> Hate-speech: Human are not created equal, some are born lesser. Counter-speech: " |
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input = tokenizer(prompt, return_tensors="pt", padding=True) |
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output_sequences = model.generate( |
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input_ids=inputs['input_ids'].to(model.device), |
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attention_mask=inputs['attention_mask'].to(model.device), |
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pad_token_id=tokenizer.eos_token_id, |
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max_length=128, |
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num_beams=3, |
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no_repeat_ngram_size=3, |
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num_return_sequences=1, |
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early_stopping=True |
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) |
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result = tokenizer.decode(output_sequences, skip_special_tokens=True) |
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#### Training Hyperparameters |
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training_args = TrainingArguments( |
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num_train_epochs=20, |
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learning_rate=3.800568576836524e-05, |
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weight_decay=0.050977894796868116, |
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warmup_ratio=0.10816909354342182, |
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optim="adamw_torch", |
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lr_scheduler_type="cosine", |
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evaluation_strategy="epoch", |
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save_strategy="epoch", |
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save_total_limit=3, |
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load_best_model_at_end=True, |
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auto_find_batch_size=True, |
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) |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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The model's performance is tested on three test sets, from which two are subsets of the [CONAN](https://github.com/marcoguerini/CONAN) dataset and one is the sexist portion of the [EDOS](https://github.com/rewire-online/edos) dataset |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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The model's performance is tested on a custom evaluation pipeline for counter-speech generation. The pipeline includes CoLA, Toxicity, Hatefulness, Offensiveness, Label and Context Similarity, Validity as Counter-Speech, Repetition Rate, target-demographic F1 and the Arithmetic Mean |
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### Results |
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CONAN |
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| Model Name | CoLA |TOX | Hate | OFF | L Sim | C Sim | VaCS | RR | F1 | AM | |
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| ---------- | ---- | -- | ---- | --- | ----- | ----- | ---- | -- | -- | -- | |
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| Human | 0.937 | 0.955 | 1.000 | 0.997 | - | 0.751 | 0.980 | 0.861 | 0.885 | 0.929 | |
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| target-aware gpt2-medium | 0.958 | 0.946 | 1.000 | 0.996 | 0.706 | 0.784 | 0.946 | 0.419 | 0.880 | 0.848 | |
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CONAN SMALL |
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| Model Name | CoLA |TOX | Hate | OFF | L Sim | C Sim | VaCS | RR | F1 | AM | |
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| ---------- | ---- | -- | ---- | --- | ----- | ----- | ---- | -- | -- | -- | |
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| Human | 0.963 | 0.956 | 1.000 | 1.000 | 1.000 | 0.768 | 0.988 | 0.995 | 0.868 | 0.949 | |
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| target-aware gpt2-medium | 0.975 | 0.931 | 1.000 | 1.000 | 0.728 | 0.783 | 0.888 | 0.911 | 0.792 | 0.890 | |
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EDOS |
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| Model Name | CoLA |TOX | Hate | OFF | C Sim | VaCS | RR | F1 | AM | |
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| ---------- | ---- | -- | ---- | --- | ----- | ---- | -- | -- | -- | |
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| target-aware gpt2-medium | 0.930 | 0.815 | 0.999 | 0.975 | 0.689 | 0.857 | 0.518 | 0.747 | 0.816| |
