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+---
+license: cc-by-4.0
+base_model: bertin-project/bertin-roberta-base-spanish
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bertin_base_climate_detection_spa
+  results: []
+datasets:
+- somosnlp/spa_climate_detection
+language:
+- es
+widget:
+- text: >
+    El uso excesivo de fertilizantes nitrogenados -un fenómeno frecuente en la
+    agricultura- da lugar a la producción de óxido nitroso, un potente gas de
+    efecto invernadero. Un uso más juicioso de los fertilizantes puede frenar
+    estas emisiones y reducir la producción de fertilizantes, que consume mucha
+    energía.
+pipeline_tag: text-classification
+---
+
+
+# Model Card for bertin_base_climate_detection_spa_v2
+
+README Spanish Version: [README_ES](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/blob/main/README_ES.md) 
+
+<p align="center">
+  <img src="https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/resolve/main/model_image_repo_380.jpg" alt="Model Illustration" width="500">
+</p>
+
+
+This model is a fine-tuning version of the model: [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) using the dataset [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection).
+The model is focused on the identification of texts on topics related to climate change and sustainability. This project was based on the English version of [climatebert/distilroberta-base-climate-detector](https://huggingface.co/climatebert/distilroberta-base-climate-detector). 
+
+The motivation of the project was to create a repository in Spanish on information or resources on topics such as: climate change, sustainability, global warming, energy, etc; the idea is to give visibility to solutions, examples of good environmental practices or news that help us to combat the effects of climate change; in a way similar to what the project [Drawdown](https://drawdown.org/solutions/table-of-solutions) does but providing examples of solutions or new research on each topic. To achieve this
+In order to achieve this objective, we consider that the identification of texts that speak about these topics is the first step. Some of the direct applications are: classification of papers and scientific publications, news, opinions.
+
+Future steps: 
+- We intend to create an advanced model that classifies texts related to climate change based on sectors (token classification), for example: classify based on electricity, agriculture, industry, transport, etc.
+- Publish a sector-based dataset.
+- Realize a Q/A model that can provide relevant information to the user on the topic of climate change.
+
+## Model Details
+
+### Model Description
+- **Developed by:** [Gerardo Huerta](https://huggingface.co/Gerard-1705) [Gabriela Zuñiga](https://huggingface.co/Gabrielaz)
+- **Funded by:** SomosNLP, HuggingFace
+- **Model type:** Language model, instruction tuned, text classification
+- **Language(s):** es-ES, es-PE
+- **License:** cc-by-nc-sa-4.0
+- **Fine-tuned from model:** [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish)
+- **Dataset used:** [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection)
+
+### Fuentes de modelos
+
+- **Repository:** [somosnlp/bertin_base_climate_detection_spa](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/tree/main) <!-- Enlace al `main` del repo donde tengáis los scripts, i.e.: o del mismo repo del modelo en HuggingFace o a GitHub. -->
+- **Demo:** [identificacion de textos sobre cambio climatico y sustentabilidad](https://huggingface.co/spaces/somosnlp/Identificacion_de_textos_sobre_sustentabilidad_cambio_climatico)
+- **Video presentation:** [Proyecto BERTIN-ClimID](https://www.youtube.com/watch?v=sfXLUP9Ei-o)
+
+## Uses
+
+### Direct Use
+- News classification: With this model it is possible to classify news headlines related to the areas of climate change.
+- Paper classification: The identification of scientific texts that disclose solutions and/or effects of climate change. For this use, the abstract of each paper can be used for identification.
+
+### Indirect Use
+- For the creation of information repositories regarding climate issues.
+- This model can serve as a basis for creating new classification systems for climate solutions to disseminate new efforts to combat climate change in different sectors.
+- Creation of new datasets that address the issue. 
+
+### Out-of-Scope Use
+- The use for text classification of unverifiable or unreliable sources and their dissemination, e.g., fake news or disinformation.
+
+## Bias, Risks, and Limitations
+En este punto no se han realizados estudios concretos sobre los sesgos y limitaciones, sin embargo hacemos los siguientes apuntes en base a experiencia previa y pruebas del modelo:
+- Hereda los sesgos y limitaciones del modelo base con el que fue entrenado, para mas detalles véase: [BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403). Sin embargo, no son tan evidentes de encontrar por el tipo de tarea en el que se esta implementando el modelo como lo es la clasificacion de texto.
+- Sesgos directos como por ejemplo el mayoritario uso de lenguaje de alto nivel en el dataset debido a que se utilizan textos extraidos de noticias, documentación legal de empresas que pueden complicar la identificación de textos con lenguajes de bajo nivel (ejemplo: coloquial). Para mitigar estos sesgos, se incluyeron en el dataset opiniones diversas sobre temas de cambio climatico extraidas de fuentes como redes sociales, adicional se hizo un rebalanceo de las etiquetas.
+- El dataset nos hereda otras limitaciones como por ejemplo: el modelo pierde rendimiento en textos cortos, esto es debido a que la mayoria de los textos utilizados en el dataset tienen una longitud larga de entre 200 - 500 palabras. Nuevamente se intentó mitigar estas limitaciones con la inclusión de textos cortos.
+
+### Recommendations
+
+- Como hemos mencionado, el modelo tiende a bajar el rendimiento en textos cortos, por lo que lo recomendable es establecer un criterio de selección de textos largos a los cuales se necesita identificar su temática.
+
+## How to Get Started with the Model
+
+```python
+## Asumiendo tener instalados transformers, torch
+from transformers import AutoModelForSequenceClassification
+import torch
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("somosnlp/bertin_base_climate_detection_spa")
+model = AutoModelForSequenceClassification.from_pretrained("somosnlp/bertin_base_climate_detection_spa")
+
+# Traduccion del label 
+id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+label2id = {"NEGATIVE": 0, "POSITIVE": 1}
+
+# Funcion de inferencia
+def inference_fun(Texto):
+    inputs = tokenizer(Texto, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_class_id = logits.argmax().item()
+    output_tag = model.config.id2label[predicted_class_id]
+    return output_tag
+
+input_text = "El uso excesivo de fertilizantes nitrogenados -un fenómeno frecuente en la agricultura- da lugar a la producción de óxido nitroso, un potente gas de efecto invernadero. Un uso más juicioso de los fertilizantes puede frenar estas emisiones y reducir la producción de fertilizantes, que consume mucha energía."
+
+print(inference_fun(input_text))
+```
+
+
+## Training Details
+
+### Training Data
+The training data were obtained from the dataset [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection).
+The training data represent about 79% of the total data in the dataset.
+
+The labels are represented as follows:
+
+Labels 1s
+
+1000   -  data on paragraphs extracted from company reports on the subject.
+
+600    -  data on various opinions, mostly short texts.
+
+Labels 0s
+
+300    -  data on paragraphs extracted from business reports not related to the subject.
+
+500   -  data on news on various topics unrelated to the subject.
+
+500   -  data on opinions on various topics unrelated to the subject.
+
+### Training Procedure
+You can check our Google Colab to review the training procedure we take: [Colab Entrenamiento](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/blob/main/entrenamiento_del_modelo.ipynb)
+The accelerate configuration is as follows:
+In which compute environment are you running?: 0
+Which type of machine are you using?: No distributed training
+Do you want to run your training on CPU only (even if a GPU / Apple Silicon / Ascend NPU device is available)? [yes/NO]:NO
+Do you wish to optimize your script with torch dynamo?[yes/NO]:NO
+Do you want to use DeepSpeed? [yes/NO]: NO
+What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:all
+Do you wish to use FP16 or BF16 (mixed precision)?: no
+
+#### Training Hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+#### Speeds, Sizes, Times
+El modelo fue entrenado en 2 epocas con una duración total de 14.22 minutos de entrenamiento, 'train_runtime': 853.6759.
+Como dato adicional: No se utilizó precision mixta (FP16 ó BF16)
+
+
+#### Resultados del entrenamiento:
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 182  | 0.1964          | 0.9551   |
+| No log        | 2.0   | 364  | 0.1592          | 0.9705   |
+
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The assessment data were obtained from the dataset [somosnlp/spa_climate_detection](https://huggingface.co/datasets/somosnlp/spa_climate_detection).
+The assessment data represent about 21% of the total data in the dataset.
+The labels are represented as follows:
+
+Labels 1s
+
+320    -  data on paragraphs extracted from company reports on the subject.
+
+160    -  data on various opinions, mostly short texts.
+
+Labels 0s
+
+80     -  data on paragraphs extracted from business reports not related to the subject.
+
+120    -  data on news on various topics unrelated to the subject.
+
+100    -  data on opinions on various topics unrelated to the subject.
+
+
+**Model reached the following results on evaluation dataset:**
+- **Loss:** 0.1592
+- **Accuracy:** 0.9705
+
+#### Metrics
+The metric was precision.
+
+### Results
+Look at the Inference section of Colab: [entrenamiento_del_modelo](https://huggingface.co/somosnlp/bertin_base_climate_detection_spa/blob/main/entrenamiento_del_modelo.ipynb)
+
+Accuracy 0.95
+Precision 0.916
+Recall 0.99
+F1 score 0.951
+
+## Environmental Impact
+Utilizando la herramienta de [ML CO2 IMPACT](https://mlco2.github.io/impact/#co2eq) calculamos que el siguiente impacto ambiental debido al entrenamiento:
+-  **Tipo de hardware:** T4 
+-  **Horas utilizadas (incluye pruebas e iteraciones para mejorar el modelo):** 4 horas
+-  **Proveedor de nube:** Google Cloud (colab)
+-  **Región computacional:** us-east
+-  **Huella de carbono emitida:** 0.1kg CO2
+
+
+## Technical Specifications
+
+#### Software
+
+- Transformers 4.39.3
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2
+
+#### Hardware
+
+- GPU equivalent to T4
+- For reference, the model was trained on the free version of Google Colab
+
+## License 
+
+cc-by-nc-sa-4.0 Due to inheritance of the data used in the dataset.
+
+## Citation
+**BibTeX:**
+```
+@software{BERTIN-ClimID,
+  author = {Gerardo Huerta, Gabriela Zuñiga},
+  title = {BERTIN-ClimID: BERTIN-Base Climate-related text Identification},
+  month = Abril,
+  year = 2024,
+  url = {https://huggingface.co/somosnlp/bertin_base_climate_detection_spa}
+}
+```
+
+## More Information
+
+This project was developed during the [Hackathon #Somos600M](https://somosnlp.org/hackathon) organized by SomosNLP. We thank all event organizers and sponsors for their support during the event.
+
+**Team:**
+
+- [Gerardo Huerta](https://huggingface.co/Gerard-1705)
+- [Gabriela Zuñiga](https://huggingface.co/Gabrielaz)
+
+## Contact
+
+- [email protected]
+- [email protected]