Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use charanhu/distilled-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use charanhu/distilled-llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="charanhu/distilled-llama")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("charanhu/distilled-llama") model = AutoModelForSequenceClassification.from_pretrained("charanhu/distilled-llama") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6363508701324463
f1_macro: 0.7318336094995542
f1_micro: 0.7487487487487487
f1_weighted: 0.7512756608041216
precision_macro: 0.7294127967332055
precision_micro: 0.7487487487487487
precision_weighted: 0.7558151474618371
recall_macro: 0.7364389688286989
recall_micro: 0.7487487487487487
recall_weighted: 0.7487487487487487
accuracy: 0.7487487487487487
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Model tree for charanhu/distilled-llama
Base model
FacebookAI/roberta-base