Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use markt23917/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use markt23917/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="markt23917/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("markt23917/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("markt23917/finetuning-sentiment-model-3000-samples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4d197b026aa889cbecadd4700e7cfef02d455e69f66316b8f4c1519cc4c4708
- Size of remote file:
- 268 MB
- SHA256:
- ca1d9dd46bceb09f92998168ed24f791fa7871b037a76b67d027d5e2a59f6c6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.