Update README.md

README.md

@@ -1,3 +1,114 @@
 ---
 license: cc-by-nc-4.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- counter speech
 ---
+
+---
+
+# Target-Aware Counter-Speech Generation
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+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.
+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.
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model is intended for generating counter-speech responses for a given hate speech sequence, combined with special tokens for target-demographic embeddings.
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+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.
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+
+    
+    types = ["MIGRANTS", "POC", "LGBT+", "MUSLIMS", "WOMEN", "JEWS", "other", "DISABLED"] # A list of all available target-demographic tokens
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    
+    model = AutoModelForCausalLM.from_pretrained(tum-nlp/gpt-2-medium-target-aware-counterspeech-generation)
+    tokenizer = AutoTokenizer.from_pretrained(tum-nlp/gpt-2-medium-target-aware-counterspeech-generation)
+    tokenizer.padding_side = "left" 
+
+    prompt = "<|endoftext|> <other> Hate-speech: Human are not created equal, some are born lesser. Counter-speech: "
+    input = tokenizer(prompt, return_tensors="pt", padding=True)
+    output_sequences = model.generate(
+            input_ids=inputs['input_ids'].to(model.device),
+            attention_mask=inputs['attention_mask'].to(model.device),
+            pad_token_id=tokenizer.eos_token_id,
+            max_length=128,
+            num_beams=3,
+            no_repeat_ngram_size=3,
+            num_return_sequences=1,
+            early_stopping=True
+        )
+      result = tokenizer.decode(output_sequences, skip_special_tokens=True)
+
+
+#### Training Hyperparameters
+
+    training_args = TrainingArguments(
+      num_train_epochs=20,
+      learning_rate=3.800568576836524e-05,
+      weight_decay=0.050977894796868116,
+      warmup_ratio=0.10816909354342182,
+      optim="adamw_torch",
+      lr_scheduler_type="cosine",
+      evaluation_strategy="epoch",
+      save_strategy="epoch",
+      save_total_limit=3,
+      load_best_model_at_end=True,
+      auto_find_batch_size=True,
+    )
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Data Card if possible. -->
+
+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
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+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
+
+
+### Results
+CONAN
+| Model Name | CoLA |TOX | Hate | OFF | L Sim | C Sim | VaCS | RR | F1 | AM |
+| ---------- | ---- | -- | ---- | --- | ----- | ----- | ---- | -- | -- | -- |
+| Human      | 0.937 | 0.955 | 1.000 | 0.997 | -  | 0.751 | 0.980 | 0.861 | 0.885 | 0.929 |
+| target-aware gpt2-medium | 0.958 | 0.946 | 1.000 | 0.996 | 0.706 | 0.784 | 0.946 | 0.419 | 0.880 | 0.848 |
+
+CONAN SMALL
+| Model Name | CoLA |TOX | Hate | OFF | L Sim | C Sim | VaCS | RR | F1 | AM |
+| ---------- | ---- | -- | ---- | --- | ----- | ----- | ---- | -- | -- | -- |
+| Human | 0.963 | 0.956 | 1.000 | 1.000 | 1.000 | 0.768 | 0.988 | 0.995 | 0.868 | 0.949 |
+| target-aware gpt2-medium | 0.975 | 0.931 | 1.000 | 1.000 | 0.728 | 0.783 | 0.888 | 0.911 | 0.792 | 0.890 |
+
+EDOS
+| Model Name | CoLA |TOX | Hate | OFF | C Sim | VaCS | RR | F1 | AM |
+| ---------- | ---- | -- | ---- | --- | ----- | ---- | -- | -- | -- |
+| target-aware gpt2-medium | 0.930 | 0.815 | 0.999 | 0.975 | 0.689 | 0.857 | 0.518 | 0.747 | 0.816| 
+
+