Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Kayvane/distilbert-complaints-wandb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kayvane/distilbert-complaints-wandb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kayvane/distilbert-complaints-wandb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kayvane/distilbert-complaints-wandb") model = AutoModelForSequenceClassification.from_pretrained("Kayvane/distilbert-complaints-wandb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kayvane/distilbert-complaints-wandb: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/Kayvane/distilbert-complaints-wandb/resolve/main/training_args.bin
- Command line
-
hf download hf://Kayvane/distilbert-complaints-wandb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kayvane/distilbert-complaints-wandb/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 37b37e49d45278debd6abf8053051d61d5cb82b6c3c1429ebe00689819342c9c
- Size of remote file:
- 3.31 kB
- SHA256:
- 5ecb22eee63366f08353ff73ec4d736eb49d7f6386793baaaf27f9e2701eae77
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