Update README.md

README.md

@@ -27,7 +27,6 @@ datasets:
 - KaggleRussianNews
 ---
 
-This is [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for __sentiment classification__ of short __Russian__ texts.
 The task is a __multi-class classification__ with the following labels:
 
 ```yaml
@@ -51,44 +50,4 @@ from transformers import pipeline
 model = pipeline(model="seara/rubert-tiny2-russian-sentiment")
 model("Привет, ты мне нравишься!")
 # [{'label': 'positive', 'score': 0.9398769736289978}]
-```
-
-## Dataset
-
-This model was trained on the union of the following datasets:
-
-- Kaggle Russian News Dataset
-- Linis Crowd 2015
-- Linis Crowd 2016
-- RuReviews
-- RuSentiment
-
-An overview of the training data can be found on [S. Smetanin Github repository](https://github.com/sismetanin/sentiment-analysis-in-russian).
-
-__Download links for all Russian sentiment datasets collected by Smetanin can be found in this [repository](https://github.com/searayeah/russian-sentiment-emotion-datasets).__
-
-## Training
-
-Training were done in this [project](https://github.com/searayeah/bert-russian-sentiment-emotion) with this parameters:
-
-```yaml
-tokenizer.max_length: 512
-batch_size: 64
-optimizer: adam
-lr: 0.00001
-weight_decay: 0
-epochs: 5
-```
-
-Train/validation/test splits are 80%/10%/10%.
-
-## Eval results (on test split)
-
-
-|         |neutral|positive|negative|macro avg|weighted avg|
-|---------|-------|--------|--------|---------|------------|
-|precision|0.7    |0.84    |0.74    |0.76     |0.75        |
-|recall   |0.74   |0.83    |0.69    |0.75     |0.75        |
-|f1-score |0.72   |0.83    |0.71    |0.75     |0.75        |
-|auc-roc  |0.85   |0.95    |0.91    |0.9      |0.9         |
-|support  |5196   |3831    |3599    |12626    |12626       |
+```