Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use AnonymousCS/bert-large-uncased-Twitter-toxicity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousCS/bert-large-uncased-Twitter-toxicity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnonymousCS/bert-large-uncased-Twitter-toxicity")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/bert-large-uncased-Twitter-toxicity") model = AutoModelForSequenceClassification.from_pretrained("AnonymousCS/bert-large-uncased-Twitter-toxicity") - Notebooks
- Google Colab
- Kaggle
CeciliaS commited on
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README.md
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- Loss: 0.6281
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- Accuracy: 0.8280
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- lr_scheduler_type: linear
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- num_epochs: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 1.0 | 70 | 0.4009 | 0.8136 |
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| 0.4614 | 2.0 | 140 | 0.3777 | 0.8172 |
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| 0.1829 | 3.0 | 210 | 0.5505 | 0.8208 |
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| 0.1829 | 4.0 | 280 | 0.6281 | 0.8280 |
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### Framework versions
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- Loss: 0.6281
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- Accuracy: 0.8280
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### Training hyperparameters
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The following hyperparameters were used during training:
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- lr_scheduler_type: linear
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- num_epochs: 4
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### Framework versions
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