Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use skrh/bert_finetuning-sentiment-model-3000-samples-label-smoothing-0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skrh/bert_finetuning-sentiment-model-3000-samples-label-smoothing-0.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="skrh/bert_finetuning-sentiment-model-3000-samples-label-smoothing-0.3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("skrh/bert_finetuning-sentiment-model-3000-samples-label-smoothing-0.3") model = AutoModelForSequenceClassification.from_pretrained("skrh/bert_finetuning-sentiment-model-3000-samples-label-smoothing-0.3", device_map="auto") - Notebooks
- Google Colab
- Kaggle