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README.md

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 # ABSA_Reproducibility
 Codes and Datasets for our ECIR 2021 Paper: "Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild"
+
+## Setup instructions
+* Create a conda environment using the requirements.txt file.
+* Alternately, one can use the ABSA.yml extracted from our conda environment to exactly replicate the environment.
+* Download and unzip the [GloVe embeddings](http://nlp.stanford.edu/data/glove.840B.300d.zip) into the current folder.
+
+## Running experiments
+```python
+python grid_search.py
+```
+
+## Evaluating results
+* Change directory to results/
+```python
+python process_results.py path [isHard]
+```
+path - Select one from [in_domain, contrast_logs, cross_domain, cross_domain_incremental]
+isHard - [Default: 'False'] is set as 'True' only if you want to evaluate hard set results for in_domain experiments,  i.e.
+```python
+python process_results.py in_domain True
+```
+
+### Additional notes
+* We run each experiment with 5 random seeds (1,2,3,4,5).
+* Our experiments were run on a Tesla P100 PCIE, 16GB GPU and CUDA 10.1 and PyTorch 1.1.0.
+* For the incremental cross domain experiments, the **--train\_dataset** argument can be set to **crossdomain\_indomain\_ratio**, for instance **Laptops_Restaurants_0.1** for evaluting the cross domain combination (Laptops - Train, Restaurants - Test).