AnnyNguyen commited on
Commit
4ccf21f
verified
1 Parent(s): 76f9223

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +75 -0
README.md ADDED
@@ -0,0 +1,75 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: vi
3
+ tags:
4
+ - emotion-recognition
5
+ - vietnamese
6
+ - bartpho
7
+ license: apache-2.0
8
+ datasets:
9
+ - VSMEC
10
+ metrics:
11
+ - accuracy
12
+ - f1
13
+ model-index:
14
+ - name: bartpho-emotion
15
+ results:
16
+ - task:
17
+ type: text-classification
18
+ name: Emotion Recognition
19
+ dataset:
20
+ name: VSMEC
21
+ type: custom
22
+ metrics:
23
+ - name: Accuracy
24
+ type: accuracy
25
+ value: <INSERT_ACCURACY>
26
+ - name: F1 Score
27
+ type: f1
28
+ value: <INSERT_F1_SCORE>
29
+ base_model:
30
+ - vinai/bartpho-syllable
31
+ pipeline_tag: text-classification
32
+ ---
33
+
34
+ # bartpho-emotion: Emotion Recognition for Vietnamese Text
35
+
36
+ This model is a fine-tuned version of [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable) on the **VSMEC** dataset for emotion recognition in Vietnamese text. It achieves state-of-the-art performance on this task.
37
+
38
+ ## Model Details
39
+
40
+ - **Base Model**: [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable)
41
+ - **Dataset**: [VSMEC](https://github.com/uitnlp/vsmec) (Vietnamese Social Media Emotion Corpus)
42
+ - **Fine-tuning Framework**: HuggingFace Transformers
43
+ - **Hyperparameters**:
44
+ - Batch size: `32`
45
+ - Learning rate: `5e-5`
46
+ - Epochs: `100`
47
+ - Max sequence length: `256`
48
+
49
+ ## Dataset
50
+
51
+ The model was trained on the **VSMEC** dataset, which contains Vietnamese social media text annotated with emotion labels. The dataset includes the following emotion categories: `<INSERT_LABELS>`.
52
+
53
+ ## Results
54
+
55
+ The model was evaluated using the following metrics:
56
+
57
+ - **Accuracy**: `<INSERT_ACCURACY>`
58
+ - **F1 Score**: `<INSERT_F1_SCORE>`
59
+
60
+ ## Usage
61
+
62
+ You can use this model for emotion recognition in Vietnamese text. Below is an example of how to use it with the HuggingFace Transformers library:
63
+
64
+ ```python
65
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
66
+
67
+ tokenizer = AutoTokenizer.from_pretrained("visolex/bartpho-emotion")
68
+ model = AutoModelForSequenceClassification.from_pretrained("visolex/bartpho-emotion")
69
+
70
+ text = "T么i r岷 vui v矛 h么m nay tr峄漣 膽岷筽!"
71
+ inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
72
+ outputs = model(**inputs)
73
+ predicted_class = outputs.logits.argmax(dim=-1).item()
74
+
75
+ print(f"Predicted emotion: {predicted_class}")