Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_km_100_v2_wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_km_100_v2_wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_100_v2_wnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_100_v2_wnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_100_v2_wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_km_100_v2_wnli: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_km_100_v2_wnli/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_km_100_v2_wnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_km_100_v2_wnli/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 4745f45097b45710d95c8c3680d1fc364b004800ee5cb94172729e6d23dbc232
- Size of remote file:
- 5.37 kB
- SHA256:
- 1bc2029e8558fa0b5f5849e991ef9a420cad4dc5d6a7b21a96b50e585d4b5901
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