Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_km_5_v1_rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_km_5_v1_rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_5_v1_rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_5_v1_rte") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_5_v1_rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_km_5_v1_rte: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_km_5_v1_rte/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_km_5_v1_rte/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_km_5_v1_rte/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- c1d3171c57780dee437dad302d77fd17820a9fa1b52274688168cba6ded6ac75
- Size of remote file:
- 5.37 kB
- SHA256:
- 2e890a725bf2c6dd54097eb2cfefb3e19ba915770bd54b4cc9c4713e2da67859
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.