Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use dinhlnd1610/distilbert-base-uncased-finetuned-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dinhlnd1610/distilbert-base-uncased-finetuned-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dinhlnd1610/distilbert-base-uncased-finetuned-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dinhlnd1610/distilbert-base-uncased-finetuned-sst2") model = AutoModelForSequenceClassification.from_pretrained("dinhlnd1610/distilbert-base-uncased-finetuned-sst2") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-sst2 / runs /Dec06_08-42-17_486c67ed138d /events.out.tfevents.1701856737.486c67ed138d.2142.5
- Xet hash:
- e3e0b74c4b6a82f406cc16b0317dba25380502be4c5f2121677d502c3bcf2156
- Size of remote file:
- 13.1 kB
- SHA256:
- 127e2815f37b3a56b72e346e0a1679a3a49c52066412b952d0a2ea76489c5106
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.