Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use sakers/hcde560-autotrain-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sakers/hcde560-autotrain-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sakers/hcde560-autotrain-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sakers/hcde560-autotrain-text-classification") model = AutoModelForSequenceClassification.from_pretrained("sakers/hcde560-autotrain-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.179272174835205
f1_macro: 0.5708204334365324
f1_micro: 0.5769230769230769
f1_weighted: 0.5684835675160753
precision_macro: 0.5746753246753247
precision_micro: 0.5769230769230769
precision_weighted: 0.5757992007992008
recall_macro: 0.5833333333333333
recall_micro: 0.5769230769230769
recall_weighted: 0.5769230769230769
accuracy: 0.5769230769230769
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Model tree for sakers/hcde560-autotrain-text-classification
Base model
typeform/distilbert-base-uncased-mnli