Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_5_v1_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_5_v1_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_5_v1_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_5_v1_stsb") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_5_v1_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_rand_5_v1_stsb: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/bert_base_rand_5_v1_stsb/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_rand_5_v1_stsb/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_rand_5_v1_stsb/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 58d44f3b8b4998f458db1e5a6963caa0aff64a5ecc50a522ee58e789f3c7f5d2
- Size of remote file:
- 5.37 kB
- SHA256:
- 9e9039fa92108c8a3943a1c5c1e5a4430eab5bf4ba872448e7bd102a306a053b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.