Multilabel classification model trained on the Toxic Comments dataset from Kaggle. (https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data)

Fine tuned using DistilBert.

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model = AutoModelForSequenceClassification.from_pretrained("pretrained_model")
tokenizer = AutoTokenizer.from_pretrained("model_tokenizer")

X_train = ["Why is Owen's retirement from football not mentioned? He hasn't played a game since 2005."]
batch = tokenizer(X_train, truncation=True, padding='max_length', return_tensors="pt")
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]

with torch.no_grad():
  outputs = model(**batch)
  predictions = torch.sigmoid(outputs.logits)*100
  probs = predictions[0].tolist()
  for i in range(len(probs)):
    print(f"{labels[i]}: {round(probs[i], 3)}%")
```

Output expected below:
```
toxic: 0.676%
severe_toxic: 0.001%
obscene: 0.098%
threat: 0.007%
insult: 0.021%
identity_hate: 0.004%
```
{"license": "mit"}
transformers pytorch safetensors distilbert text-classification license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

Multilabel classification model trained on the Toxic Comments dataset from Kaggle. (URL

Fine tuned using DistilBert.



Output expected below:

TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
