Update README.md

README.md

@@ -1,3 +1,211 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- es
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- lgbt
+- classification
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+1. The process begins by recovering discriminatory phrases for the LGBTQIA+ community from Twitter, Instagram and Tiktok (197 phrases) available at 
+[https://zenodo.org/records/13756092] 
+2. Then preprocess them, label them by 3 evaluator raters como lgbtfobic (1) and non-lgbtfobic (0)
+3. Augmentation was necessary applying backtranslation and synonyms (1200 phrases) available at .
+4. Then adjust the BETO model based on BERT (dccuchile/bert-base-spanish-wwm-uncased) for discriminatory phrase detection for the LGBT community.
+5. In this way the lgbetO v1.0.0 model was generated.
+
+- **Developed by:** [Martínez-Araneda, C; Segura Navarrete, A.; Gutierrez Valenzuela, Mariella; Maldonado Mintiel, Diego; Gómez Meneses, P.; Vidal-Castro; Christian  ]
+- **Model type:** [text-classification]
+- **Language(s) (NLP):** [Spanish]
+- **License:** [cc-by-4.0]
+- **Finetuned from model [dccuchile/bert-base-spanish-wwm-uncased]:** More information of base model [https://github.com/dccuchile/beto]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. 
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+-->
+## Uses
+
+This model can be used to detect offensive and discriminatory language against lgbt community. It could be used as a moderation service in forums and digital spaces.
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+This model has its own bias from having been adjusted with a small data set.<!-- 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
+#lybraries
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+# Define la ruta de donde cargarás el modelo
+#load_directory = "./lgbetO"
+# Cargar el modelo entrenado
+#model = AutoModelForSequenceClassification.from_pretrained(load_directory)
+# Cargar el tokenizer
+#tokenizer = AutoTokenizer.from_pretrained(load_directory)
+## Training Details
+
+The training process begins by retrieving offensive/non-offensive and discriminatory/non-discriminatory language against phrases related to the lgbt community from twitter, instagram and tiktok, 
+preprocessing them, labeling them by 3 raters, augmenting them with backtranslation and synonyms, 
+and adjusting the BETO base model (dccuchile/bert-base -spanish-wwm-uncased) for discriminatory phrase detection for the lgbt community.
+
+### Training Data
+
+https://zenodo.org/records/13756092
+
+### 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 Dataset 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:** Google Cloud Platform [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** southamerica
+- **Carbon Emitted:** 0.14kgCO$_2$eq/kWh
+
+
+Experiments were conducted using Google Cloud Platform in region southamerica-east1, which has a carbon efficiency of 0.2 kgCO$_2$eq/kWh. A cumulative of 10 hours of computation was performed on hardware of type T4 (TDP of 70W).
+
+Total emissions are estimated to be 0.14 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
+    
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+(GPU) del backend de Google Compute Engine en Python 3
+
+#### Hardware
+RAM: 3.87 GB/12.67 GB
+Disco: 33.96 GB/112.64 GB
+
+#### 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
+
+[cmartinez@ucsc.cl]