
### Label Scheme

| Component | Labels |
| --- | --- |
| **`textcat_multilabel`** | `negative`, `neutral`, `positive` |

### Accuracy

| Type | Score |
| --- | --- |
| `CATS_SCORE` | 100.00 |
| `CATS_MACRO_P` | 98.32 |
| `CATS_MACRO_R` | 99.06 |
| `CATS_MACRO_F` | 98.68 |
| `CATS_MACRO_AUC` | 100.00 |

### Performance

| METHOD                 | SCORE    | MARCO_P  | MARCO_R  | MARCO_F1  |
|------------------------|----------|----------|----------|-----------|
| RAW                    | 0.999969 | 0.983160 | 0.990559 | 0.986817  |
| Remove Emoji           | 0.999981 | 0.991312 | 0.998311 | 0.994768  |
| Emoji to Description   | 0.999898 | 0.989309 | 0.981696 | 0.985458  |
| Remove Punctuation     | 0.999239 | 0.991312 | 0.998311 | 0.994768  |

| CATEGORY | PRECISION | RECALL    | F1        |
|----------|-----------|-----------|-----------|
| negative | 0.996534  | 0.996534  | 0.996534  |
| neutral  | 0.954545  | 0.976744  | 0.965517  |
| positive | 0.998400  | 0.998400  | 0.998400  |
{"language": ["zh"], "tags": ["spacy", "text-classification"], "datasets": ["scfengv/TVL_Sentiment_Analysis"], "metrics": ["accuracy"], "widget": [{"text": "\u5c0f\u6e29\u52a0\u6cb9\uff01\u7f8e\u6d25\u6fc3\u52a0\u6cb9\ud83d\udd25\ud83d\udd25"}], "pipeline_tag": "text-classification", "model-index": [{"name": "zh_Sentiment_classifier", "results": []}]}
spacy text-classification zh dataset:scfengv/TVL_Sentiment_Analysis region:us