Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_50_v2_qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_50_v2_qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_50_v2_qqp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_50_v2_qqp") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_50_v2_qqp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hartunka/bert_base_rand_50_v2_qqp: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Hartunka/bert_base_rand_50_v2_qqp/resolve/main/training_args.bin
- Command line
-
hf download hf://Hartunka/bert_base_rand_50_v2_qqp/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hartunka/bert_base_rand_50_v2_qqp/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- ba11c50c37ec560b4f29626399301197f04eae39c68ddba01368d9d69c07fd8a
- Size of remote file:
- 5.37 kB
- SHA256:
- faa736351efddcee802ef6ad07f111cdc1dff74b105bf47e234cbbff6a3c678f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.