Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_20_v2_rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_20_v2_rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_20_v2_rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_20_v2_rte") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v2_rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_rand_20_v2_rte: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_20_v2_rte/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_20_v2_rte/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_rand_20_v2_rte/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 922fd820f42b86832843665381a4c78976b4cc99e2741b3716496da45fa57844
- Size of remote file:
- 5.37 kB
- SHA256:
- c1d12a3e0a8e0e72304426f087a49304206ff6888e97bee459eef7cead0cf781
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.