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_mrpc 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_mrpc 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_mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_10_v2_mrpc") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_10_v2_mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/tiny_bert_rand_10_v2_mrpc: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_10_v2_mrpc/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_10_v2_mrpc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/tiny_bert_rand_10_v2_mrpc/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- b3ec4f3de8d2d1e15a6d9c580bd42191fad8d00d2cb26a939ff68885c1664129
- Size of remote file:
- 5.37 kB
- SHA256:
- eba8afba5d9fbd6fc5bbc0f272039cf8b4a72339c8acd2f03246766da8e3cdf9
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