Instructions to use rooftopcoder/bart-base-coqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rooftopcoder/bart-base-coqa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rooftopcoder/bart-base-coqa") model = AutoModelForSeq2SeqLM.from_pretrained("rooftopcoder/bart-base-coqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from rooftopcoder/bart-base-coqa: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/rooftopcoder/bart-base-coqa/resolve/481e99a60c1b60bcb2fb15322342ca40c7facd68/eval_results.json
- Command line
-
hf download hf://rooftopcoder/bart-base-coqa@481e99a60c1b60bcb2fb15322342ca40c7facd68/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/rooftopcoder/bart-base-coqa/resolve/481e99a60c1b60bcb2fb15322342ca40c7facd68/eval_results.json
261 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.09259259259259259, | |
| "eval_f1": 0.05973409306742639, | |
| "eval_loss": 0.7936302423477173, | |
| "eval_runtime": 2.005, | |
| "eval_samples": 54, | |
| "eval_samples_per_second": 26.933, | |
| "eval_steps_per_second": 1.995 | |
| } |