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.1701858263.486c67ed138d.2142.6
- Xet hash:
- 0408b778dc63ecd2b1be62d629214876fa167f988295aeddfb1834609a7d5191
- Size of remote file:
- 417 Bytes
- SHA256:
- 46de7f2a1259a615400c007866f23438610451cfd3527bd1118c1607e32abe48
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