Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_10_v2_qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_10_v2_qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_10_v2_qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v2_qnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v2_qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_rand_10_v2_qnli: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_10_v2_qnli/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_10_v2_qnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_rand_10_v2_qnli/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- b2680d73183ec480ce8988892c97588558bfab0f692165047eb3f868b7d9bace
- Size of remote file:
- 5.37 kB
- SHA256:
- 0e9cdd5371f79bfef1ac993ac6e54fcf2103f5906e9c7270e4792c820869190c
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