update readme with emissions information

README.md

@@ -31,4 +31,36 @@ Achieving accuracy and reliability in this process will make the second goal of
 
 ## The Model
 
-After preliminary experiments with sequential neural network models using [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model), [term frequency-inverse document frequency](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (tf-idf), and custom word embedding encoding, I settled on using a pretrained BERT model developed by [Devlin et al. 2018](https://arxiv.org/abs/1810.04805v2). Specifically, I'm using [Hugging Face's DistilBert base multilingual (cased) model](https://huggingface.co/distilbert/distilbert-base-multilingual-cased), which is based on work by [Sanh et al. 2020](https://doi.org/10.48550/arXiv.1910.01108).
+After preliminary experiments with sequential neural network models using [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model), [term frequency-inverse document frequency](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (tf-idf), and custom word embedding encoding, I settled on using a pretrained BERT model developed by [Devlin et al. 2018](https://arxiv.org/abs/1810.04805v2). Specifically, I'm using [Hugging Face's DistilBert base multilingual (cased) model](https://huggingface.co/distilbert/distilbert-base-multilingual-cased), which is based on work by [Sanh et al. 2020](https://doi.org/10.48550/arXiv.1910.01108).
+
+## Emissions
+
+Here is the `codecarbon` output from training on Google Colab with an A100 runtime:
+
+```properties
+timestamp: 2024-12-23T17:37:16
+project_name: codecarbon
+run_id: a2b8975b-512b-4158-b41f-2a00d1d6fb39
+experiment_id: 5b0fa12a-3dd7-45bb-9766-cc326314d9f1
+duration (seconds): 877.531339527
+emissions (kilograms of carbon): 0.0260658391490936
+emissions_rate (kg/sec): 2.970359914797282e-05
+cpu_power (average in watts): 42.5
+gpu_power (average in watts): 71.5115170414632
+ram_power (average in watts): 31.30389261245728
+cpu_energy (total watts): 0.0103517333061409
+gpu_energy (total watts): 0.03961337474623
+ram_energy (total watts): 0.007623585574942
+energy_consumed (total watts): 0.057588693627313
+os: Linux-6.1.85+-x86_64-with-glibc2.35
+python_version: 3.10.12
+codecarbon_version: 2.8.2
+cpu_count: 12
+cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz
+gpu_count: 1
+gpu_model: 1 x NVIDIA A100-SXM4-40GB
+ram_total_size: 83.47704696655273
+tracking_mode: machine
+on_cloud: N
+pue: 1.0
+```