# 🪐 spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the [GoEmotions dataset](https://github.com/google-research/google-research/tree/master/goemotions)

| Feature | Description |
| --- | --- |
| **Name** | `en_textcat_goemotions` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `textcat_multilabel` |
| **Components** | `transformer`, `textcat_multilabel` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [GoEmotions dataset](https://github.com/google-research/google-research/tree/master/goemotions) |
| **License** | `MIT` |
| **Author** | [Explosion](explosion.ai) |


> The dataset that this model is trained on has known flaws described [here](https://github.com/google-research/google-research/tree/master/goemotions#disclaimer) as well as label errors resulting from [annotator disagreement](https://www.youtube.com/watch?v=khZ5-AN-n2Y). Anyone using this model should be aware of these limitations of the dataset.

### Label Scheme

<details>

<summary>View label scheme (28 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`textcat_multilabel`** | `admiration`, `amusement`, `anger`, `annoyance`, `approval`, `caring`, `confusion`, `curiosity`, `desire`, `disappointment`, `disapproval`, `disgust`, `embarrassment`, `excitement`, `fear`, `gratitude`, `grief`, `joy`, `love`, `nervousness`, `optimism`, `pride`, `realization`, `relief`, `remorse`, `sadness`, `surprise`, `neutral` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `CATS_SCORE` | 90.22 |
| `CATS_MICRO_P` | 66.67 |
| `CATS_MICRO_R` | 47.81 |
| `CATS_MICRO_F` | 55.68 |
| `CATS_MACRO_P` | 55.00 |
| `CATS_MACRO_R` | 41.93 |
| `CATS_MACRO_F` | 46.29 |
| `CATS_MACRO_AUC` | 90.22 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TRANSFORMER_LOSS` | 83.51 |
| `TEXTCAT_MULTILABEL_LOSS` | 4549.84 |

{"language": ["en"], "license": "mit", "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_textcat_goemotions", "results": []}]}
spacy text-classification en license:mit region:us
TAGS
#spacy #text-classification #en #license-mit #region-us 

spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the GoEmotions dataset
============================================================================================================================================================




> 
> The dataset that this model is trained on has known flaws described here as well as label errors resulting from annotator disagreement. Anyone using this model should be aware of these limitations of the dataset.
> 
> 
> 


### Label Scheme



View label scheme (28 labels for 1 components)


### Accuracy




### Label Scheme



View label scheme (28 labels for 1 components) ### Accuracy
TAGS
#spacy #text-classification #en #license-mit #region-us 
 ### Label Scheme



View label scheme (28 labels for 1 components) ### Accuracy
passage: TAGS
#spacy #text-classification #en #license-mit #region-us 
### Label Scheme



View label scheme (28 labels for 1 components)### Accuracy