Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BaxterAI/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaxterAI/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaxterAI/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("BaxterAI/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- May23_01-27-18_adcfe59299a9
- May23_01-51-10_adcfe59299a9
- May23_02-06-52_adcfe59299a9
- May23_05-57-08_adcfe59299a9
- May23_09-38-41_2d362c5ccea5
- May23_09-40-55_2d362c5ccea5
- May23_10-13-14_2d362c5ccea5
- May23_10-13-52_2d362c5ccea5
- May23_21-36-51_f9b56511b52a
- May23_22-23-24_f9b56511b52a
- May24_00-31-54_ece63acfea1f