Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fin-tuned
+base_model: hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fine-tuned1.1
 tags:
 - generated_from_trainer
 metrics:
@@ -18,8 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # SER_wav2vec2-large-xlsr-53_240304_fin-tuned_2
 
-This model is a fine-tuned version of [hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fin-tuned2.0](https://huggingface.co/hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fin-tuned2.0) on an unknown dataset.
-It achieves the following results on the evaluation set:
+This model is a fine-tuned version of [hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fin-tuned2.0](https://huggingface.co/hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fin-tuned2.0) on an [Speech Emotion Recognition (en)](https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en) dataset.
+
+This dataset includes the 4 most popular datasets in English: Crema, Ravdess, Savee, and Tess, containing a total of over 12,000 .wav audio files. Each of these four datasets includes 6 to 8 different emotional labels.
+
+This achieves the following results on the evaluation set:
 - Loss: 1.0601
 - Accuracy: 0.6731
 - Precision: 0.6761
@@ -28,7 +31,12 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The model was obtained through feature extraction using [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) and underwent several rounds of fine-tuning. It predicts the 7 types of emotions contained in speech, aiming to lay the foundation for subsequent use of human micro-expressions on the visual level and context semantics under LLMS to infer user emotions in real-time.
+
+Although the model was trained on purely English datasets, post-release testing showed that it also performs well in predicting emotions in Chinese and French, demonstrating the powerful cross-linguistic capability of the [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) pre-trained model.
+
+emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+
 
 ## Intended uses & limitations
 
@@ -36,7 +44,7 @@ More information needed
 
 ## Training and evaluation data
 
-More information needed
+70/30 of entire dataset.
 
 ## Training procedure