Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_10_v1_rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_10_v1_rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_10_v1_rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_10_v1_rte") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_10_v1_rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_rand_10_v1_rte: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/bert_base_rand_10_v1_rte/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_rand_10_v1_rte/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_rand_10_v1_rte/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- dd6121b843f44a0781f5975e8882cd82b789e9300115ed7a03cf8f67f654c16c
- Size of remote file:
- 5.37 kB
- SHA256:
- 9a17f7f4f44d69871656465ccb0fa83d2e1913f39564953d5f178072da940e5c
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