Update README.md

README.md

@@ -63,7 +63,7 @@ This project was inspired by https://huggingface.co/datasets/alexandrainst/scand
 
 ### Filtering disclaimer. Toxicity and bias
 
-The dataset is provided as is and high likely includes toxic, biased etch. material. You should carefully curate this dataset for your needs.
+The dataset is provided as is and high likely includes toxic, biased etch. material. You should carefully curate this dataset for your needs. To label toxic messages, we used Finnish toxicity classifier [TurkuNLP/bert-large-finnish-cased-toxicity](https://huggingface.co/TurkuNLP/bert-large-finnish-cased-toxicity) released by TurkuNLP. This dataset includes 6 different toxicity labels with their predicted scores for each message. You can use those labels and scores to filter out toxic messages. 
 We evaluated subreddits with over 500 messages and decided to provide a list that based on our fast analysis should be filtered out:
 
 [FinlandOnlyfans, 
@@ -100,16 +100,23 @@ The dataset is available in Finnish
 An example from the dataset looks as follows.
 ```
 {
- 'subreddit': 'arkisuomi',
- 'created_utc': 1671152007,
- 'score': 1, 
- 'body': 'oatlyn iKaffe on maitoa parempaa kahvissa, en jois pelkästään kuitenkaan',
- 'predicted_language': '__label__fi',
- 'probability': 0.9783772230148317,
- 'year': 2022.0,
- 'day': 16.0,
- 'month': 12.0,
- 'time': '00:53:27'}
+   "subreddit": "arkisuomi",
+   "created_utc": 1671152007,
+   "score": 1,
+   "body": "oatlyn iKaffe on maitoa parempaa kahvissa, en jois pelkästään kuitenkaan",
+   "predicted_language": "__label__fi",
+   "probability": 0.9783772230148317,
+   "year": 2022.0,
+   "day": 16.0,
+   "month": 12.0,
+   "time": "00:53:27",
+   "label_identity_attack": 0.00018978118896484375,
+   "label_insult": 0.00058746337890625,
+   "label_obscene": 0.00142669677734375,
+   "label_severe_toxicity": 6.723403930664062e-05,
+   "label_threat": 0.0004100799560546875,
+   "label_toxicity": 0.01025390625
+}
 ```
 
 ### Data Fields
@@ -117,13 +124,22 @@ An example from the dataset looks as follows.
 The data fields are the same among all splits.
 
 - `subreddit`: `string`
-- `created_utc: `int`
-- `predicted_language`: a `string` 
-- `probability`: a `float64` 
-- 'year': `datetime`
-- 'day': `datetime`
-- 'month': `datetime`
-- 'time': `datetime` }
+- `created_utc: `int64`
+- `score`: `int64`
+- `body`: `string`
+- `predicted_language`: `string` 
+- `probability`: `float64` 
+- `year`: `float64`
+- `day`: `float64`
+- `month`: `float64`
+- `time`: `string`
+- `label_identity_attack`: `float64`
+- `label_insult`: `float64`
+- `label_obscene`: `float64`
+- `label_severe_toxicity`: `float64`
+- `label_threat`: `float64`
+- `label_toxicity`: `float64`
+
 
 ### Language Distribution
 - fi:  4,476,667