Add model's weights

README.md

@@ -1,116 +1,199 @@
 ---
-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
+language: en
 ---
 
-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.
+# Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-0
 
-# 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.
+<!-- Provide a quick summary of what the model is/does. -->
 
-# 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
+## Model Details
 
-First you might need the transformers version 4.30.2.
+### Model Description
 
-```
-pip install transformers==4.30.2
-```
+<!-- Provide a longer summary of what this model is. -->
 
-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
+- **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]
 
-        return embeddings, logits
-```
+### Model Sources [optional]
 
-Then, we instantiate the model as:
+<!-- Provide the basic links for the model. -->
 
-```python
-from transformers import AutoModel, AutoTokenizer, AutoConfig
+- **Repository:** https://github.com/huggingface/huggingface_hub
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-repo_name = "BenjaminOcampo/peace_cont_bert"
+## Uses
 
-config = AutoConfig.from_pretrained(repo_name)
-contrastive_model = ContrastiveModel(AutoModel.from_config(config))
-tokenizer = AutoTokenizer.from_pretrained(repo_name)
-```
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-Finally, to load the weights of the model we do as follows:
+### Direct Use
 
-```python
-model_tmp_file = hf_hub_download(repo_id=repo_name, filename="model.pt", token=read_token)
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-state_dict = torch.load(model_tmp_file)
+[More Information Needed]
 
-contrastive_model.load_state_dict(state_dict)
-```
+### Downstream Use [optional]
 
-You can make predictions as any pytorch model:
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-```
-import torch
+[More Information Needed]
 
-text = "Are you sure that Islam is a peaceful religion?"
-inputs = tokenizer(text, return_tensors="pt")
+### Out-of-Scope Use
 
-with torch.no_grad():
-    _, logits = contrastive_model(inputs["input_ids"], inputs["attention_mask"])
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-probabilities = torch.softmax(logits, dim=1)
-_, predicted_labels = torch.max(probabilities, dim=1)
-```
+[More Information Needed]
 
-# 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.
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### 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]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## 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. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## 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]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
 
-# 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)

config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "BenjaminOcampo/model-contrastive-bert__trained-in-ishate__seed-0",
+  "architectures": [
+    "ContrastiveModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.pt

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+size 440370701

pytorch_model.bin

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special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,13 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}