Summarization
Transformers
PyTorch
TensorBoard
Safetensors
English
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use sudoLife/tst-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sudoLife/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="sudoLife/tst-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sudoLife/tst-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("sudoLife/tst-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_gen_len": 73.87305505685218, | |
| "eval_loss": 1.6418160200119019, | |
| "eval_rouge1": 41.607, | |
| "eval_rouge2": 19.2272, | |
| "eval_rougeL": 29.4514, | |
| "eval_rougeLsum": 38.8228, | |
| "eval_runtime": 1579.4831, | |
| "eval_samples": 13368, | |
| "eval_samples_per_second": 8.464, | |
| "eval_steps_per_second": 1.058 | |
| } |