update model card README.md

README.md

@@ -1,39 +1,37 @@
 ---
-language:
-  - uz
-tags:
-  - transformers
-  - uzroberta
-  - uzbek
-  - latin
 license: apache-2.0
-widget:
-  - text: Menga yoqdi, juda yaxshi ekan.
+tags:
+- generated_from_trainer
 metrics:
-- precision
-- recall
-- f1
 - accuracy
+- f1
 model-index:
 - name: uzroberta-sentiment-analysis
   results: []
 ---
 
-# uzroberta-sentiment-analysis
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-This is a roBERTa-base model trained on ~23K reviews (more than 323K words) and finetuned for sentiment analysis of customer reviews. This model is built as part of author's project at the Uz-NLP 2022 Hackathon and it is suitable for Uzbek language.
+# uzroberta-sentiment-analysis
 
-<b>Labels</b>: LABEL_0 -> Negative;   LABEL_1 -> Positive
+This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0473
+- Accuracy: 0.8257
+- F1: 0.8287
 
 ## Model description
 
-This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta) on the [Uzbek App reviews for Sentiment Classification](https://github.com/SanatbekMatlatipov/uzbek-sentiment-analysis) dataset. It achieves the following results on the evaluation set:
-- Loss: 0.5718
-- Precision: 0.9113
-- Recall: 0.8869
-- F1 Score: 0.8989
-- Accuracy: 0.896
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
@@ -42,27 +40,27 @@ This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://hu
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
 - train_batch_size: 16
-- eval_batch_size: 32
+- eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 4
-- mixed_precision_training: Native AMP
+- lr_scheduler_type: linear
+- num_epochs: 6
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1595        | 1.0   | 1125 | 0.4438          | 0.8971    | 0.8523 | 0.8741 | 0.872    |
-| 0.1891        | 2.0   | 2250 | 0.4157          | 0.8961    | 0.9012 | 0.8987 | 0.894    |
-| 0.1201        | 3.0   | 3375 | 0.5024          | 0.9074    | 0.8830 | 0.8950 | 0.892    |
-| 0.0772        | 4.0   | 4500 | 0.5718          | 0.9113    | 0.8869 | 0.8989 | 0.896    |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.3411        | 1.0   | 1156 | 0.4061          | 0.8276   | 0.8303 |
+| 0.2319        | 2.0   | 2312 | 0.4074          | 0.8336   | 0.8359 |
+| 0.1629        | 3.0   | 3468 | 0.5250          | 0.8394   | 0.8416 |
+| 0.1082        | 4.0   | 4624 | 0.8780          | 0.8180   | 0.8219 |
+| 0.0712        | 5.0   | 5780 | 0.9741          | 0.8321   | 0.8349 |
+| 0.0537        | 6.0   | 6936 | 1.0473          | 0.8257   | 0.8287 |
 
 
 ### Framework versions
 
-- Transformers 4.20.1
-- Pytorch 1.12.0+cu116
-- Datasets 2.3.2
+- Transformers 4.27.3
+- Pytorch 2.0.0+cu117
+- Datasets 2.11.0
 - Tokenizers 0.12.1