Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_20_v2_rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_20_v2_rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_20_v2_rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_20_v2_rte") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_20_v2_rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from Hartunka/bert_base_rand_20_v2_rte: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/Hartunka/bert_base_rand_20_v2_rte/resolve/main/trainer_state.json
- Command line
-
hf download hf://Hartunka/bert_base_rand_20_v2_rte/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/Hartunka/bert_base_rand_20_v2_rte/resolve/main/trainer_state.json
3.63 kB
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| "is_hyper_param_search": false, | |
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| "is_world_process_zero": true, | |
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