Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use pkovvk/tahohovai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pkovvk/tahohovai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pkovvk/tahohovai")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pkovvk/tahohovai") model = AutoModelForSequenceClassification.from_pretrained("pkovvk/tahohovai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.34192928671836853
f1_macro: 0.9114377237610704
f1_micro: 0.9111111111111111
f1_weighted: 0.9114377237610702
precision_macro: 0.912826420890937
precision_micro: 0.9111111111111111
precision_weighted: 0.912826420890937
recall_macro: 0.9111111111111111
recall_micro: 0.9111111111111111
recall_weighted: 0.9111111111111111
accuracy: 0.9111111111111111
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Model tree for pkovvk/tahohovai
Base model
google-bert/bert-base-uncased