BenjaminOcampo
/

peace_cont_bert

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 ---
-language: en
 ---
-# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-0
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** BenjaminOcampo
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** https://github.com/huggingface/huggingface_hub
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-### How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-#### Hardware
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-## Citation [optional]
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-## Glossary [optional]
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 ---
+base_model: BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-0
+datasets:
+- ISHate
+language:
+- en
+library_name: transformers
+license: bsl-1.0
+metrics:
+- f1
+- accuracy
+tags:
+- hate-speech-detection
+- implicit-hate-speech
 ---
+This model card documents the demo paper "PEACE: Providing Explanations and
+Analysis for Combating Hate Expressions" accepted at the 27th European
+Conference on Artificial Intelligence: https://www.ecai2024.eu/calls/demos.
+# The Model
+This model is a hate speech detector fine-tuned specifically for detecting
+implicit hate speech. It is based on the paper "PEACE: Providing Explanations
+and Analysis for Combating Hate Expressions" by Greta Damo, Nicolás Benjamín
+Ocampo, Elena Cabrio, and Serena Villata, presented at the 27th European
+Conference on Artificial Intelligence.
+# Training Parameters and Experimental Info
+The model was trained using the ISHate dataset, focusing on implicit data.
+Training parameters included:
+- Batch size: 32
+- Weight decay: 0.01
+- Epochs: 4
+- Learning rate: 2e-5
+For detailed information on the training process, please refer to the [model's
+paper](https://aclanthology.org/2023.findings-emnlp.441/).
+# Usage
+First you might need the transformers version 4.30.2.
+```
+pip install transformers==4.30.2
+```
+This model was created using pytorch vanilla. In order to load it you have to use the following Model Class.
+```python
+class ContrastiveModel(nn.Module):
+    def __init__(self, model):
+        super(ContrastiveModel, self).__init__()
+        self.model = model
+        self.embedding_dim = model.config.hidden_size
+        self.fc = nn.Linear(self.embedding_dim, self.embedding_dim)
+        self.classifier = nn.Linear(self.embedding_dim, 2)  # Classification layer
+    def forward(self, input_ids, attention_mask):
+        outputs = self.model(input_ids, attention_mask)
+        embeddings = outputs.last_hidden_state[:, 0]  # Use the CLS token embedding as the representation
+        embeddings = self.fc(embeddings)
+        logits = self.classifier(embeddings)  # Apply classification layer
+        return embeddings, logits
+```
+Then, we instantiate the model as:
+```python
+from transformers import AutoModel, AutoTokenizer, AutoConfig
+repo_name = "BenjaminOcampo/peace_cont_bert"
+config = AutoConfig.from_pretrained(repo_name)
+contrastive_model = ContrastiveModel(AutoModel.from_config(config))
+tokenizer = AutoTokenizer.from_pretrained(repo_name)
+```
+Finally, to load the weights of the model we do as follows:
+```python
+model_tmp_file = hf_hub_download(repo_id=repo_name, filename="model.pt", token=read_token)
+state_dict = torch.load(model_tmp_file)
+contrastive_model.load_state_dict(state_dict)
+```
+You can make predictions as any pytorch model:
+```
+import torch
+text = "Are you sure that Islam is a peaceful religion?"
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    _, logits = contrastive_model(inputs["input_ids"], inputs["attention_mask"])
+probabilities = torch.softmax(logits, dim=1)
+_, predicted_labels = torch.max(probabilities, dim=1)
+```
+# Datasets
+The model was trained on the [ISHate dataset](https://huggingface.co/datasets/BenjaminOcampo/ISHate), specifically
+the training part of the dataset which focuses on implicit hate speech.
+# Evaluation Results
+The model's performance was evaluated using standard metrics, including F1 score
+and accuracy. For comprehensive evaluation results, refer to the linked paper.
+Authors:
+- [Greta Damo](https://grexit-d.github.io/damo.greta.github.io/)
+- [Nicolás Benjamín Ocampo](https://www.nicolasbenjaminocampo.com/)
+- [Elena Cabrio](https://www-sop.inria.fr/members/Elena.Cabrio/)
+- [Serena Villata](https://webusers.i3s.unice.fr/~villata/Home.html)