Update README.md

README.md

@@ -52,18 +52,55 @@ There are 8 aspects to define review in this dataset. I am the only annotator fo
 
 Take note that few reviews contain language and content that some people may find offensive, discriminatory, or inappropriate. I **DO NOT** endorse, condone or promote any of such language and content.
 
-## Model benchmark
+# Model benchmark
 
-Coming soon.
+Model benchmark on Steam Review aspect dataset split into 3 categories,
 
-## Download
+* Base: Non-attention based language model.
+* Embedding: Inspired by MTEB, obtained embedding trained on Logistic Regressor for up to 100 epochs.
+* Fine-tune.
+
+Source code for running these models is available on [GitHub](https://github.com/ilos-vigil/steam-review-aspect-dataset/tree/main/model_benchmark). But take note it may not follow best practice as it was written with the goal of using it only once. I ran those on Linux, RTX 3060 and 32GB RAM.
+
+> Base
+
+| Model              | Macro precision | Macro recall | Macro F1 | Note                                                                         |
+| ------------------ | --------------- | ------------ | -------- | ---------------------------------------------------------------------------- |
+| Spacy Bag of Words | 0.6203          | 0.5391       | 0.5494   |                                                                              |
+| FastText           | 0.6284          | 0.5713       | 0.5871   | Minimum text preprocessing, use pretrained vector                            |
+| FastText           | 0.6933          | 0.5821       | 0.6027   | Minimum text preprocessing, choose hyperparameter based on K-5 fold autotune |
+| Spacy Ensemble     | 0.6043          | 0.6773       | 0.6299   | Choose hyperparameter based on simple grid search                            |
+
+> Embedding
+
+| Model                                                     | Param | Max tokens | Macro precision | Macro recall | Macro F1 | Note                                 |
+| --------------------------------------------------------- | ----- | ---------- | --------------- | ------------ | -------- | ------------------------------------ |
+| sentence-transformers/all-mpnet-base-v2                   | 110M  | 514        | 0.7074          | 0.5431       | 0.5853   |                                      |
+| jinaai/jina-embeddings-v2-small-en                        | 137M  | 8192       | 0.7068          | 0.6075       | 0.6437   |                                      |
+| jinaai/jina-embeddings-v2-base-en                         | 137M  | 8192       | 0.6813          | 0.6501       | 0.6618   |                                      |
+| Alibaba-NLP/gte-large-en-v1.5                             | 434M  | 8192       | 0.7001          | 0.6501       | 0.6729   |                                      |
+| nomic-ai/nomic-embed-text-v1.5                            | 137M  | 8192       | 0.7075          | 0.6498       | 0.6756   |                                      |
+| McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised | 7111M | 32768      | 0.7238          | 0.6697       | 0.6928   | NF4 double quantization, instruction |
+| WhereIsAI/UAE-Large-V1                                    | 335M  | 512        | 0.7245          | 0.6718       | 0.6946   |                                      |
+| mixedbread-ai/mxbai-embed-large-v1                        | 335M  | 512        | 0.7215          | 0.6817       | 0.6989   |                                      |
+| intfloat/e5-mistral-7b-instruct                           | 7111M | 32768      | 0.7345          | 0.7000       | 0.7137   | NF4 double quantization, instruction |
+
+> Fine-tune
+
+| Model                             | Param | Max tokens | Macro precision | Macro recall | Macro F1 | Note                                            |
+| --------------------------------- | ----- | ---------- | --------------- | ------------ | -------- | ----------------------------------------------- |
+| jinaai/jina-embeddings-v2-base-en | 137M  | 8192       | 0.7485          | 0.7257       | 0.7354   | Choose hyperparameter from Ray Tune (30 trials) |
+| Alibaba-NLP/gte-large-en-v1.5     | 434M  | 8192       | 0.8403          | 0.8152       | 0.8231   | Choose hyperparameter from Ray Tune (16 trials) |
+
+
+# Download
 
 You can download Steam review aspect dataset from here (HuggingFace) or one of these sources,
 
 * [GitHub](https://github.com/ilos-vigil/steam-review-aspect-dataset)
 * [Kaggle](https://www.kaggle.com/datasets/ilosvigil/steam-review-aspect-dataset)
 
-## Citation
+# Citation
 
 If you wish to use this dataset in your research or project, please cite this blog post: [Steam review aspect dataset](https://srec.ai/blog/steam-review-aspect-dataset) 
 
@@ -85,6 +122,6 @@ For those who need it, a BibTeX citation format also has been prepared.
 }
 ```
 
-## License
+# License
 
 Steam Review aspect dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0).