Update README.md

README.md

@@ -18,42 +18,34 @@ should probably proofread and complete it, then remove this comment. -->
 
 # sentiment-polish-gpt2-small
 
-This model was trained from polish-gpt2-small on the polemo2-official dataset.
+This model was trained from [polish-gpt2-small](https://huggingface.co/sdadas/polish-gpt2-small) on the [polemo2-official](https://huggingface.co/datasets/clarin-pl/polemo2-official) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.4659
 - Accuracy: 0.9627
 
 ## Model description
 
-Traned from https://huggingface.co/sdadas/polish-gpt2-small
+Trained from [polish-gpt2-small](https://huggingface.co/sdadas/polish-gpt2-small)
 
 ## Intended uses & limitations
 
 More information needed
 
 ## Training and evaluation data
-https://huggingface.co/datasets/clarin-pl/polemo2-official
-```bibtex
-@inproceedings{kocon-etal-2019-multi,
-    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
-    author = "Koco{\'n}, Jan  and
-      Mi{\l}kowski, Piotr  and
-      Za{\'s}ko-Zieli{\'n}ska, Monika",
-    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
-    month = nov,
-    year = "2019",
-    address = "Hong Kong, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/K19-1092",
-    doi = "10.18653/v1/K19-1092",
-    pages = "980--991",
-    abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
-}
+Merged all rows from [polemo2-official](https://huggingface.co/datasets/clarin-pl/polemo2-official) dataset.
+
+Train/test split: 80%/20%
+
+Datacollator:
+```py
+from transformers import DataCollatorWithPadding
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="longest", max_length=128, pad_to_multiple_of=8)
 ```
 
 ## Training procedure
 
 GPU: RTX 3090
+
 Training time: 2:53:05
 
 ### Training hyperparameters