Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use John-7S/autotrain-99iib-7om2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use John-7S/autotrain-99iib-7om2v with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="John-7S/autotrain-99iib-7om2v")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("John-7S/autotrain-99iib-7om2v") model = AutoModelForSequenceClassification.from_pretrained("John-7S/autotrain-99iib-7om2v") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.7546173334121704
f1_macro: 0.2714285714285714
f1_micro: 0.35714285714285715
f1_weighted: 0.2714285714285714
precision_macro: 0.25
precision_micro: 0.35714285714285715
precision_weighted: 0.25
recall_macro: 0.35714285714285715
recall_micro: 0.35714285714285715
recall_weighted: 0.35714285714285715
accuracy: 0.35714285714285715
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Model tree for John-7S/autotrain-99iib-7om2v
Base model
google-bert/bert-base-uncased