Instructions to use hsuvaskakoty/bart_temp_dm_10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsuvaskakoty/bart_temp_dm_10k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hsuvaskakoty/bart_temp_dm_10k") model = AutoModelForSeq2SeqLM.from_pretrained("hsuvaskakoty/bart_temp_dm_10k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_progress_scores.csv from hsuvaskakoty/bart_temp_dm_10k: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
-
https://huggingface.co/hsuvaskakoty/bart_temp_dm_10k/resolve/main/training_progress_scores.csv
- Command line
-
hf download hf://hsuvaskakoty/bart_temp_dm_10k/training_progress_scores.csv
-
curl -L -o training_progress_scores.csv https://huggingface.co/hsuvaskakoty/bart_temp_dm_10k/resolve/main/training_progress_scores.csv
205 Bytes
| global_step,eval_loss,train_loss | |
| 625,0.7744697078168392,0.6349814534187317 | |
| 1250,0.8020930242836475,0.5394045114517212 | |
| 1875,0.8247375644445419,0.48130670189857483 | |
| 2500,0.8606873774826527,0.2800898849964142 | |