Update README.md

README.md

@@ -14,33 +14,16 @@ tags:
 # Mistral Fine-Tuned on not Engaging with Hate Speech
 
 ## Model Description
-This model is a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.2` on a hate speech dataset using the PEFT approach, to prevent the model from exacerbating hate discourse.
+This model is a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.1` on a hate speech dataset using the PEFT approach, to prevent the model from exacerbating hate discourse.
 
 ## Intended Uses & Limitations
 This model is intended for research purposes in conversational applications to stop hate speech generation.
 
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-
-
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Biases**: The model may carry biases present in the training data.
+- **False Positives/Negatives**: It's not perfect and may continue some hate speech conversations.
+- **Domain Specificity**: Performance may vary across different domains.
 
 ### Recommendations
 
@@ -74,32 +57,27 @@ Use the code below to get started with the model.
 
 ## 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]
-
+- **Hardware Type:** RTX A6000
+- **Hours used:** 9
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Efficiency (kg/kWh):** 0,432
+- **Carbon Emitted (kg eq. CO2):** 1,17
 
-## 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. -->
+## Citation
 
-**BibTeX:**
-
-[More Information Needed]
+If you use this model, please cite the following reference:
 
-**APA:**
-
-[More Information Needed]
+```bibtex
+@article{
+  SOON!
+}
+```
 
 ## Training procedure
 
-
 The following `bitsandbytes` quantization config was used during training:
 - quant_method: bitsandbytes
 - _load_in_8bit: False
@@ -117,5 +95,7 @@ The following `bitsandbytes` quantization config was used during training:
 
 ### Framework versions
 
+- PEFT 0.6.2
 
-- PEFT 0.6.2
+## Acknowledgements
+The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).