Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use rsingh1911/fine-tune-LLM-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsingh1911/fine-tune-LLM-Models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rsingh1911/fine-tune-LLM-Models")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rsingh1911/fine-tune-LLM-Models") model = AutoModelForSequenceClassification.from_pretrained("rsingh1911/fine-tune-LLM-Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.8639428615570068
f1_macro: 0.19444444444444445
f1_micro: 0.2857142857142857
f1_weighted: 0.19444444444444445
precision_macro: 0.20748299319727895
precision_micro: 0.2857142857142857
precision_weighted: 0.20748299319727895
recall_macro: 0.2857142857142857
recall_micro: 0.2857142857142857
recall_weighted: 0.2857142857142857
accuracy: 0.2857142857142857
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Model tree for rsingh1911/fine-tune-LLM-Models
Base model
google-bert/bert-base-uncased