Update README.md

README.md

@@ -15,7 +15,6 @@ tags:
 - emotion
 - russian
 - rubert
-- tiny
 - sentiment
 - sentiment-analysis
 - classification
@@ -25,7 +24,7 @@ datasets:
 - cedr
 ---
 
-This is [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for __emotion classification__ of short __Russian__ texts.
+This is [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned for __emotion classification__ of short __Russian__ texts.
 The task is a __multi-label classification__ with the following labels:
 
 ```yaml
@@ -52,9 +51,9 @@ anger: злость
 
 ```python
 from transformers import pipeline
-model = pipeline(model="seara/rubert-tiny2-cedr")
+model = pipeline(model="seara/rubert-base-cased-cedr-russian-emotion")
 model("Привет, ты мне нравишься!")
-# [{'label': 'joy', 'score': 0.9605025053024292}]
+# [{'label': 'joy', 'score': 0.9388909935951233}]
 ```
 
 ## Dataset
@@ -68,20 +67,19 @@ An overview of the training data can be found in the source [article](https://ww
 Training were done in this [project](https://github.com/searayeah/vkr-bert) with this parameters:
 
 ```yaml
-tokenizer.max_length: null
 batch_size: 64
 optimizer: adam
 lr: 0.00001
 weight_decay: 0
-num_epochs: 30
+num_epochs: 5
 ```
 
 ## Eval results (on test split)
 
 |         |no_emotion|joy   |sadness|surprise|fear   |anger|micro avg|macro avg|weighted avg|
 |---------|----------|------|-------|--------|-------|-----|---------|---------|------------|
-|precision|0.82      |0.84  |0.84   |0.79    |0.78   |0.55 |0.81     |0.77     |0.8         |
-|recall   |0.84      |0.83  |0.85   |0.66    |0.67   |0.33 |0.78     |0.7      |0.78        |
-|f1-score |0.83      |0.83  |0.84   |0.72    |0.72   |0.41 |0.79     |0.73     |0.79        |
-|auc-roc  |0.92      |0.96  |0.96   |0.91    |0.91   |0.77 |0.94     |0.91     |0.93        |
+|precision|0.87      |0.84  |0.85   |0.74    |0.7    |0.66 |0.83     |0.78     |0.83        |
+|recall   |0.84      |0.86  |0.82   |0.71    |0.74   |0.33 |0.79     |0.72     |0.79        |
+|f1-score |0.86      |0.85  |0.84   |0.72    |0.72   |0.44 |0.81     |0.74     |0.8         |
+|auc-roc  |0.95      |0.97  |0.96   |0.94    |0.93   |0.86 |0.95     |0.93     |0.95        |
 |support  |734       |353   |379    |170     |141    |125  |1902     |1902     |1902        |