Update README.md

README.md

@@ -14,25 +14,43 @@ should probably proofread and complete it, then remove this comment. -->
 
 # minilm-finetuned-movie
 
-This model is a fine-tuned version of [microsoft/miniLM-L12-H384-uncased](https://huggingface.co/microsoft/miniLM-L12-H384-uncased) on an unknown dataset.
+This model is a fine-tuned version of [microsoft/miniLM-L12-H384-uncased](https://huggingface.co/microsoft/miniLM-L12-H384-uncased) on sasingh192/movie-review dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0451
 - F1: 0.9856
 
 ## Model description
 
-More information needed
+This model can be used to categorize a movie review into of the following categories:
+0 - negative
+1 - somewhat negative
+2 - neutral
+3 - somewhat positive
+4 - positive
 
 ## Intended uses & limitations
 
-More information needed
+The fined model is based on the finetuning of the model devloped by Wang et al. 
+
+@misc{wang2020minilm,
+    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
+    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
+    year={2020},
+    eprint={2002.10957},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+
 
 ## Training and evaluation data
 
-More information needed
+sasingh192/movie-review dataset contains a column 'TrainValTest'. The values provied in this columns are 'Train', 'Val', and 'Test'.
+The dataset can be filtered for the 'Train' values to train the model. Evaluation can be perfored on the data filtered by 'Val'. 'Test' is used as a blind test for kaggle.
 
 ## Training procedure
 
+Training details are listed below.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: