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_qnli 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_qnli 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_qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v1_qnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v1_qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_rand_5_v1_qnli: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_qnli/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v1_qnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_rand_5_v1_qnli/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 77208fbdace1f35a1011bafefc94ffc22e61cac2aa62cfa7ed837855bb59ac0a
- Size of remote file:
- 5.37 kB
- SHA256:
- 523f9f7da98c4a78b098d787c44f7a97ab40dec2b2d4ad4486be36dd0195d02d
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