Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_km_5_v2_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_v2_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_v2_rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_5_v2_rte") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_5_v2_rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_km_5_v2_rte: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_km_5_v2_rte/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_km_5_v2_rte/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_km_5_v2_rte/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- d56a9c23824eea83042e592bd3eeab99550af5c9837296770b52802d99e27a52
- Size of remote file:
- 5.37 kB
- SHA256:
- 4b179d37b56fdf295a9210a07af82f3fda865eda91acfb5a2f7297bce0d2f438
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