Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use RobCaamano/toxicity_weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobCaamano/toxicity_weighted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RobCaamano/toxicity_weighted")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RobCaamano/toxicity_weighted") model = AutoModelForSequenceClassification.from_pretrained("RobCaamano/toxicity_weighted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0240
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- Train Precision: 0.9522
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- Train Recall: 0.9190
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- Epoch:
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## Model description
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0240
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- Train Precision: 0.9522
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- Train Recall: 0.9190
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- Epoch: 11
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## Model description
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