Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use zkava01/TrainingCont with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zkava01/TrainingCont with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zkava01/TrainingCont")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zkava01/TrainingCont") model = AutoModelForSequenceClassification.from_pretrained("zkava01/TrainingCont", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6183168888092041
f1_macro: 0.7924817520251889
f1_micro: 0.7988165680473372
f1_weighted: 0.7985787394401608
precision_macro: 0.7954105120958426
precision_micro: 0.7988165680473372
precision_weighted: 0.798566036140332
recall_macro: 0.7897774650823431
recall_micro: 0.7988165680473372
recall_weighted: 0.7988165680473372
accuracy: 0.7988165680473372
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