Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v1_wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_5_v1_wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v1_wnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v1_wnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v1_wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_rand_5_v1_wnli: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_wnli/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v1_wnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_wnli/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 03ce0c0347e5b38e80bbfb43d686d3181125d7d40bd805d6f1ddb0272e087861
- Size of remote file:
- 5.37 kB
- SHA256:
- aba48e36e412207b643b80230a6a7320f827221a1e576bdac404cc3c19f87891
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.