Instructions to use TheLongSentance/t5_large_baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheLongSentance/t5_large_baseline with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("TheLongSentance/t5_large_baseline") model = AutoModelForSeq2SeqLM.from_pretrained("TheLongSentance/t5_large_baseline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_results.json from TheLongSentance/t5_large_baseline: direct link, hf CLI and curl.
- Browser
- Download file 343 Bytes
-
https://huggingface.co/TheLongSentance/t5_large_baseline/resolve/main/predict_results.json
- Command line
-
hf download hf://TheLongSentance/t5_large_baseline/predict_results.json
-
curl -L -o predict_results.json https://huggingface.co/TheLongSentance/t5_large_baseline/resolve/main/predict_results.json
343 Bytes
| { | |
| "predict_gen_len": 46.705, | |
| "predict_loss": 0.0036411203909665346, | |
| "predict_rouge1": 99.6781, | |
| "predict_rouge2": 99.5066, | |
| "predict_rougeL": 99.6729, | |
| "predict_rougeLsum": 99.6874, | |
| "predict_runtime": 101.703, | |
| "predict_samples": 200, | |
| "predict_samples_per_second": 1.967, | |
| "predict_steps_per_second": 0.492 | |
| } |