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README.md

@@ -20,7 +20,7 @@ widget:
 
 # Twitter emotion PL (base)
 
-Twitter emotion PL (base) is a model based on [herbert-base](https://huggingface.co/allegro/herbert-base-cased) for analyzing emotion of Polish twitter posts. It was trained on the translated version of [TweetEval](https://www.researchgate.net/publication/347233661_TweetEval_Unified_Benchmark_and_Comparative_Evaluation_for_Tweet_Classification) by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu.
+Twitter emotion PL (base) is a model based on [distiluse](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) for analyzing emotion of Polish twitter posts. It was trained on the translated version of [TweetEval](https://www.researchgate.net/publication/347233661_TweetEval_Unified_Benchmark_and_Comparative_Evaluation_for_Tweet_Classification) by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu.
 
 The model will give you a three labels: positive, negative and neutral.
 
@@ -35,17 +35,17 @@ nlp = pipeline("text-classification", model="bardsai/twitter-emotion-pl-base")
 nlp("Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl")
 ```
 ```bash
-[{'label': 'joy', 'score': 0.5163766145706177}]
+[{'label': 'joy', 'score': 0.7068771123886108}]
 ```
 
 ## Performance
 | Metric | Value |
 | --- | ----------- |
-| f1 macro | 0.756 |
-| precision macro | 0.767 |
-| recall macro | 0.750 |
-| accuracy | 0.789 |
-| samples per second | 131.6 |
+| f1 macro | 0.692 |
+| precision macro | 0.700 |
+| recall macro | 687 |
+| accuracy | 0.737 |
+| samples per second | 255.2 |
 
 (The performance was evaluated on RTX 3090 gpu)