Text Classification
Transformers
PyTorch
Safetensors
distilbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use DracoHugging/Distilbert-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DracoHugging/Distilbert-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DracoHugging/Distilbert-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DracoHugging/Distilbert-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("DracoHugging/Distilbert-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from DracoHugging/Distilbert-sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/DracoHugging/Distilbert-sentiment-analysis/resolve/main/training_args.bin
- Command line
-
hf download hf://DracoHugging/Distilbert-sentiment-analysis/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/DracoHugging/Distilbert-sentiment-analysis/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 349666bd6a4ccb9006c30809860a01499a230ab69849f40aee74368b0a5a9871
- Size of remote file:
- 3.9 kB
- SHA256:
- 0e884709121056b8a8b74404145470c3d6284cd62e29135a095db1867a35f6ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.