Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use zkava01/RoBERTa_Trained_Oct29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zkava01/RoBERTa_Trained_Oct29 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zkava01/RoBERTa_Trained_Oct29")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zkava01/RoBERTa_Trained_Oct29") model = AutoModelForSequenceClassification.from_pretrained("zkava01/RoBERTa_Trained_Oct29", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.007734560873359442
f1_macro: 0.9991256596070146
f1_micro: 0.9989545216936748
f1_weighted: 0.9989547969603347
precision_macro: 0.9989517819706499
precision_micro: 0.9989545216936748
precision_weighted: 0.9989578093613047
recall_macro: 0.9993019197207679
recall_micro: 0.9989545216936748
recall_weighted: 0.9989545216936748
accuracy: 0.9989545216936748
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