Summarization
Transformers
PyTorch
TensorBoard
English
pegasus
text2text-generation
Generated from Trainer
Instructions to use ChaniM/tst-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChaniM/tst-summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="ChaniM/tst-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ChaniM/tst-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ChaniM/tst-summarization") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_gen_len": 27.10166068222621, | |
| "eval_loss": 2.7112603187561035, | |
| "eval_rouge1": 25.387, | |
| "eval_rouge2": 9.0306, | |
| "eval_rougeL": 17.5963, | |
| "eval_rougeLsum": 22.0487, | |
| "eval_runtime": 6327.8863, | |
| "eval_samples": 13368, | |
| "eval_samples_per_second": 2.113, | |
| "eval_steps_per_second": 1.056 | |
| } |